11institutetext: Cluster of Excellence “Machine Learning in Science”, Tübingen
University, Germany, 11email<EMAIL_ADDRESS>22institutetext: Department of Statistics, LMU Munich, Germany, 22email:
<EMAIL_ADDRESS>
#  Dear XAI Community, We Need to Talk!
Fundamental Misconceptions in Current XAI Research

Timo Freiesleben 11 Gunnar König 22

###### Abstract

Despite progress in the field, significant parts of current XAI research are
still not on solid conceptual, ethical, or methodological grounds.
Unfortunately, these unfounded parts are not on the decline but continue to
grow. Many explanation techniques are still proposed without clarifying their
purpose. Instead, they are advertised with ever more fancy-looking heatmaps or
only seemingly relevant benchmarks. Moreover, explanation techniques are
motivated with questionable goals, such as building trust, or rely on strong
assumptions about the ’concepts’ that deep learning algorithms learn. In this
paper, we highlight and discuss these and other misconceptions in current XAI
research. We also suggest steps to make XAI a more substantive area of
research.

###### Keywords:

XAI Interpretable Machine Learning

##  1 Introduction

This is an unusual paper from start to end. We don’t start the paper with
generic examples of great Machine Learning (ML) achievements. The thoughts in
this paper are directed at people who are already working on eXplainable
Artificial Intelligence (XAI), so we are long past promotional talks. Our
goals with this paper are twofold: 1. to highlight misconceptions within parts
of the XAI community in past and current research; 2. to provide constructive
feedback and steps forward to make XAI a scientific discipline that actually
improves ML transparency.

After wrapping our heads around XAI-related topics for a couple of years, we
became increasingly frustrated whenever we attended a workshop or conference
on the topic. We do not claim that no progress is being made or that no high-
quality research is being conducted. However, we are saddened that many
computational, intellectual, and financial resources are being poured into
projects that, in our view, do not stand on solid grounds:

  * •

proposals for new interpretation techniques that serve no clear purpose

  * •

anecdotal evidence from intuitive-looking heatmaps or ”benchmarks” on
seemingly relevant criteria are used as a substitute for a clear motivation

  * •

explanations are generated that mislead humans into trusting ML models without
the models being trustworthy

Instead of swallowing our frustration, we decided to canalize it into this
paper with the hope of helping researchers avoid such projects that might be
technically interesting but conceptionally unfounded. We believe that such a
debate is especially urgent since funding for XAI research is inexorably high,
and the community is ever-growing. Without clear purposes and proper
conceptual foundations, the XAI boom could lead to a bubble endangered to
implode. We would like to see our field become a pillar of ML transparency
rather than the ML trust-washing machine.

The perspective we will take is more of a philosophical bird’s eye view of XAI
research. It is not our style to expose specific papers by pointing out their
flaws. We also feel that this is not necessary because the misconceptions
discussed are ’elephants in the room’ in our community. Before sharing our
thoughts, we would like to point the reader to work that guided our
perspective on XAI and that may help to underpin our arguments.

##  2 Related Work

Many papers criticize XAI on various grounds, and we believe many of the
criticisms still apply to current XAI. We focus on the critiques that most
impacted the community and/or our thoughts.

In his seminal paper, Zachary Lipton argues that XAI lacks a proper problem
formulation and that this problem must be tackled to make progress as a field
[28]. Instead of a well-defined goal, XAI offers a potpourri of motivations
for explainability, such as increasing trust, fairness, or understanding.
Summarized, he argues that: “When we have solid problem formulations, flaws in
methodology can be addressed by articulating new methods. But when the problem
formulation itself is flawed, neither algorithms nor experiments are
sufficient to address the underlying problem.” [28, p.8]

Finale Doshi-Velez and Been Kim highlight the problem of assessing the quality
of explanations and comparing different explanation techniques. They describe
three potential standards for evaluation: application, human, and functionally
grounded interpretability, the first two rely on human studies and the third
one on formal model properties [10]. They posit the intuitive principle that
“the claim of the research should match the type of the evaluation.” [10, p.9]

Cynthia Rudin provides examples of post-hoc explanations that can mislead the
user because they are difficult to interpret [36]. She argues that this issue
becomes particularly threatening when the stakes are high, and model
authorities have a financial interest in model opacity. Rudin and her co-
authors point out that: “interpretable models do not necessarily create or
enable trust – they could also enable distrust. They simply allow users to
decide whether to trust them.” [37, p.6] In consequence, they argue in favor
of inherently interpretable models.

Our views on XAI have also been strongly shaped by philosophical discussions
around explanation and interpretability. Philosophers gave formal accounts of
what constitutes an explanatory relationship, namely a statement about the
phenomenon to be explained (called the _explanandum_), a statement about a
phenomenon that explains the explanandum (called the _explanans_), and an
_explanatory link_ between explanans and explanandum [47, 19]. For formalizing
the explanatory link, especially causal accounts dominate, where the explanans
is a difference maker with respect to the explanandum [46].

Krishnan rightfully highlights the importance of distinguishing the causal
explanatory from the justificatory role of explanations. She notes that the
two may often not align in the context of XAI as we might face explanations
that do not justify decisions and justifications that do not explain them
[24]. Others have emphasized the different explananda present in XAI, are we
interested in explaining the model or the modeled real-world phenomenon [12,
42, 45]? Finally, Erasmus, Brunet, and Fisher argued that many statements may
formally explain a phenomenon, however, it is often difficult to interpret
these explanations correctly [11].

##  3 Misconceptions in XAI Research

In this section, we highlight the key misconceptions we see present in current
XAI research and illustrate them in little caricatures. For many of these
misconceptions, we are not the first to identify them. However, these
misconceptions have persisted over time despite strong and convincing
criticism. We see nothing wrong in repeating true things that are still
ignored by parts of our community.

### Misconception 1: “Explanation Methods are Purpose-Free”

Many ’explanation methods’ are presented as mathematical constructs without a
conceptual or practical justification. Usually, such papers have the following
storyline:

  1. 1.

ML models are black-boxes

  2. 2.

Explanations are needed because of [trust, transparency, detecting bugs, etc.]

  3. 3.

Here are some formalisms, theorems, and the implementation

  4. 4.

Look at the nice [images, text annotations, plots, etc.], don’t they look
exactly how you would expect them?

  5. 5.

In this arbitrary benchmark we invented, our method is much better than all
the others in ’explaining’.

However, it remains unclear why anyone should call these images or plots
explanations in the first place. Worse, it even remains unclear what purpose
these ’explanations’ might serve and under what conditions they are helpful.

Figure 1: Misconception that explanation methods are purpose-free

We do not claim that explanations can serve only one purpose, but rather that
they should serve at least one purpose. Moreover, it should be shown, or at
least clearly motivated, how exactly the proposed explanation technique serves
this purpose. One may contend here that we do science for science’s sake; the
purpose is knowledge. However, as long as we do not have a widely accepted
definition of explainability or interpretability, a purpose is the only way to
connect explainability techniques with the real world. ’Explanation
techniques’ that are not motivated by any practical purpose should be
suspicious to our community. If you cannot think of any context in which your
explanation helps potential explainees (i.e. the recipients of explanations),
this is a good indication that you should trash the technique.

### Misconception 2: “One Explanation Technique to Rule Them All”

There is a persistent belief in our community that we only need to find and
research the single best explanation technique (e.g., SHAP), choose the best
hyperparameters (e.g., the ideal baseline), and then we will always have the
best explanations that provide perfect understanding. However, the goals we
pursue with explanations are diverse: we may want to audit the model, learn
something about the modeled phenomenon, debug models, or provide end-users
with the ability to contest the model’s decision or act based on it. Depending
on the goal, an entirely different technique, with different hyperparameters
choices and additional side constraints may be appropriate.

Figure 2: Misconception that there is one true explanation technique

Explanation Purposes are generally in conflict. Counterfactual explanations
are the ideal example to illustrate these conflicts and the trade-offs we must
make [23]. In the original paper by Wachter et al [44], counterfactuals are
presented as explanations that provide understanding, contestability, and
recourse. If we think of algorithmic recourse (counterfactuals that guide
human actions to reach a desired outcome), the actionability of features is
crucial; for example, humans cannot simply become younger to reach the desired
outcome. Thus, age is not part of counterfactuals tailored for recourse.
Discrimination based on age, on the other side, might be a good reason to
contest a decision. That is why, age can surely be part of a counterfactual
tailored for contesting. Finally, for the vague purpose of understanding the
ML model, counterfactuals might not be the right tool at all, as they only
provide extremely limited insight into the model.

### Misconception 3: “Benchmarks do not Need a Ground-Truth”

Benchmarks are meant to be objective comparisons between competitors according
to a universally agreed standard. Machine learners love benchmarks. Benchmarks
have been the bread and butter in ML research in the last decade and an
important pillar for progressing the field. Because of the success of
benchmarking in ML, the XAI community figured that benchmarks should be a
central part of our field as well. Unfortunately, in XAI we generally lack the
central element we have in supervised ML to make objective comparisons – a
ground truth. Without a ground-truth, it is hard to come up with metrics that
quantify desirable properties and that are widely agreed upon.

Figure 3: Misconception that we can have benchmarks without ground-truth

Accepting the problem of the missing ground truth, there would have been two
ways for progress in XAI: 1. abandon the idea of benchmarks in XAI altogether
and move toward a more qualitative evaluation of explanations; 2. define
benchmarks through the explanation purpose, i.e., how well does the
explanation serve that purpose, which gives us again some notion of ground-
truth. Parts of our community, however, have taken less rocky paths:
Regardless of the explanation purpose, and with little conceptual motivation,
they formally define properties that they are optimizing their explanations
for. Other explanation techniques (often designed for completely different
applications and optimized for distinct desiderata) are then compared
according to their own standards. In this form, benchmarks lose their
justification; they become advertisement space rather than an objective
standard for comparison.

### Misconception 4: “We Should Give People Explanations They Find Intuitive”

Many papers in our field use standards to motivate explanations that we find
particularly questionable. For instance, explainees are given images or
annotations that should convince them that the explanation technique actually
highlights the right things. The images and annotations are tailored to look
compelling and intuitive, conveying a message like – “You see, the model is
actually looking at the parts of the object that you also look at when
performing the task; you can trust this.” As a consequence, we (over-)fit
explanation techniques to human intuition; however, the question is whether
these ’explanations’ are still faithful to the explained ML model.

Figure 4: Misconception that the goal is to give people explanations they find
intuitive

We think that a categorical mistake is made here; XAI should help make the
model mechanism more transparent, not compel people into believing the system
is good. Explanations provide grounds to decide whether to trust the model;
they should not be designed to compel people into trusting the model. We
should distinguish between an _explanation_ of a decision and a
_justification_ of a decision. Justifications are good reasons for a decision;
Explanations are the actual reasons for a decision [2, 24]. They may align in
decisions where the actual reasons for a decision can be ethically justified.
In XAI, however, they very often diverge. Think of cases where an ’explainer’
’explains’ the predictions of the prediction model without any access to it
beyond the single prediction. Or, when the evaluation standard for
explanations is which kinds of explanations people like better. Indeed, it can
be argued that people also often provide only justifications for their
actions, but do not provide their actual reasons or are often not even aware
of them. However, this is not an argument for why we should accept the same
for XAI explanations; instead, we should strive for higher standards,
explanations that are faithful to the causal decision-making process [16].

### Misconception 5: “Current Deep Nets Accidentally Learn Human Concepts”

Big parts of our field share the following, in our opinion unwarranted,
presupposition: Deep neural nets learn the same concepts as humans. The idea
is that early layers learn low-level concepts, such as edges in images or
syllables in sound; Layers closer to the output on the other side learn high-
level concepts, such as the concept of a wheel or the concept of a noun [34].
Concepts are assumed to be learned without explicitly forcing the model to
learn such concepts, but only by optimizing the model to classify images or
correctly complete sentences. The assumption is that the only way to solve
complex tasks is to use exactly the concepts that humans use [5]. Thus, all we
need to do is to train the network and then use XAI techniques like activation
maximization or network dissection to discover/reveal which nodes in the
network stand for which concept, and then – tada – we have a fully transparent
model where every part of the model stands for something, and the model
basically does logical reasoning again [33].

We agree that this would be fantastic; however, for the following reasons, we
are far more pessimistic concerning the conceptual reasoning in neural nets:

  * •

Many regularization techniques, for instance, dropout [41], explicitly force
the model to represent in a distributed manner by punishing overreliance on
individual neurons.

  * •

Even though research showed that some nodes in the network co-activate in the
presence of certain concepts (actually, the co-activation in percentage is far
less impressive than one would think), the causal role of the concept is not
shared [4, 32, 43, 9, 13]. That means that for instance cutting the neuron in
a bird classifier that ’represents’ wings or intervening on it does not or
only marginally change the model’s performance/prediction when birds with
different wings are presented. Is this really what we mean when we talk about
representing concepts?

  * •

One of the reasons why humans have shared concepts is because they need to
effectively communicate with other humans about the world [15, 40, 35].
However, effective communication has not been a constraint in the training of
ML models. Also, humans do not face one but a variety of different tasks. For
simple classifications, abstract concepts are not needed as there exist
shortcuts [14].

Figure 5: Misconception that current deep nets accidentally learn human
concepts

Fancy images like those generated by activation maximization techniques [34,
31] should not fool us in this regard: Just because the images generated have
some wing-like elements does not mean that they represent wings. Not only are
the images we get extremely sensitive to the source image on which we perform
activation maximization [34], but they are likely to contain other forms and
small shapes that we, as humans, blend out. For instance, research on
adversarial examples indicates that deep nets use features in their
classification that humans do not attend to [21]. It is questionable whether
we as humans will ever understand the ’concepts’ of ML models [6].

### Misconception 6: “Every XAI Paper Needs Human Studies”

Many pointed to the importance of human studies in making progress on XAI [27,
10, 8]. We agree that evaluating the quality of explanations based on their
impact on human performance on a particular task (to which the explanations
are tailored) is reasonable and solid research. However, when it comes to
explaining a specific phenomenon, at least two distinct questions must be
addressed [29]: 1. What counts conceptually as an explanation for the
phenomenon? 2. Which among the explanations for the phenomenon are good
explanations for a specific explainee? While the latter question requires
properly designed human studies, the former does not; instead, it’s a
philosophical/conceptual question that can be addressed with conceptual
analysis and formal mathematical tools.

Why is the conceptual definition of what counts as an explanation important at
all? Why can’t we go directly to the second step and test explanations in the
real world, with real human explainees? In principle we could do that, but in
practice the space of possible ’explanations’ is unlimited. Conceptualizing
what counts as an explanation for a phenomenon is building up the theory
needed for an informed search for good explanations. In many cases where human
studies are conducted, a more careful conceptual analysis would have been
advisable.

Figure 6: Misconception that every XAI paper needs human studies

More generally, not conducting human studies does not mean dismissing
explanation evaluation. For instance, a purely formal evaluation of
explanation techniques can be justified if human studies have already been
conducted for that type of explanation. Also, not all purposes of XAI require
conducting human studies. For example, if we want to use XAI to estimate a
specific quantity using the model, the speed and accuracy by which this
quantity is measured allows us to compare it with other estimators estimating
the same quantity [30].

### Misconception 7: “XAI Methods can be Wrong”

Many papers have recently shown how saliency-based or model-agnostic
explanation techniques like SHAP, LIME, counterfactuals can be ’tricked’ to
provide any desired explanation [39, 26, 1, 22]. This has been taken as major
arguments against these techniques and led to arguments why the techniques are
wrong or questioning their reliability [26, 36, 37, 45]. To us, there seem to
be misunderstandings concerning the consequences of these lines of research.

Figure 7: Misconception that XAI methods can be wrong

While we allow for arbitrary model and data complexity, we require that
explanations be simple. Therefore, explanations will indeed not be faithful to
every aspect of the model. In this sense, they do nothing wrong; they describe
the formal aspects they describe. The fact that explanations are not faithful
to every model aspect is the motivation for having different kinds of XAI
techniques, each illuminating a different aspect while neglecting another. You
may be able to fool SHAP, you may be able to fool LIME, but you won’t be able
to fool all techniques all the time. It is difficult to find the right level
of abstraction in a given context: easily interpretable and local explanations
like counterfactuals might have too little expressive power, they can be
manipulated without changing much of the overall model behavior; more abstract
and global explanations like partial dependence plots may zoom out too far,
thereby allowing to hide problematic behavior in the specifics of the model.

The fact that small model modifications can mislead explanation techniques is
nevertheless important – it shows that the XAI techniques we have and the
explanations they provide are very hard to interpret. We may need more diverse
evidence to draw conclusions based on XAI explanations. Our field should take
this as a call for developing XAI techniques on all levels of abstraction,
describing all aspects of behavior relevant to real-world purposes.

### Misconception 8: “Extrapolating to Stay True to the Model”

Most XAI techniques rely on probing the ML model in one way or the other: LIME
is locally sampling inputs, predicts them, and fits a linear model;
counterfactuals search for close input points from a desired predicted class;
Permutation feature importance (PFI) permutes the values in a specific feature
and measures the drop in performance due to this permutation; Activation
maximization uses gradient descent to find an input that maximally triggers a
specific unit; integrated gradients approximate the integral over the path
integral between the ’explained’ image and a baseline image.

Figure 8: Misconception that model evaluation in extrapolation regions is
unproblematic

The problem is not THAT the model is probed, but WHERE – namely in areas where
it has not seen any data, i.e., in areas where the model has to extrapolate
[20]. ML models are notoriously bad at extrapolating to completely unseen
instances [17, 3, 18]. In extrapolation regions, models disagree even when
fitted to exactly the same data and achieve similar high performance on a test
set. Asking an ML model to extrapolate is like asking a five-year-old kid who
hasn’t gone to school about her insights into algebraic topology. You might
get an answer, but that answer will not really help you.

Recent literature argues that explanations that rely on extrapolation are true
to the model, while those that only probe the model within the data manifold
are true to the data [7].111If we stay within the manifold, the model
explanations can even be interpreted in terms of the data-generating mechanism
[12]. Clearly, since the model is defined for instances outside of the
manifold, probing the model in these areas will give us further insight into
the model (for purposes such as debugging or robustness checks) that we would
not have gained otherwise. However, we believe that for most XAI purposes, we
are interested in the behavior of the model in areas where it is (at least
putatively) qualified. As soon as we leave the data manifold, the
interpretation of explanation techniques becomes very blurry. We think it is
highly problematic for the interpretation of current explanation methods that
they rely so strongly on extrapolation.

##  4 Steps Forward

We hope that these misconceptions show: XAI is still a pre-paradigmatic
discipline [25]. We cannot simply adopt some arbitrary assumptions and move on
to paradigmatic scientific problem-solving. We must fight about the right
conceptions of what the field is about, the language we should use, and the
right evaluation strategies. We know that it is very easy to be critical while
it is very difficult to be constructive. So we want to share at least some
thoughts and intuitions about how we think the field should evolve to become a
more substantive discipline.

### Step 1: Go From Purpose to Benchmark

Explanation techniques should start with a purpose. Again, this does not mean
that they can only serve one purpose, but they should show that they serve at
least one purpose. A purpose is a goal humans have in mind when they ask for
explanations. Once the purpose is fixed, the evaluation of the explanations
follows naturally. Your explanation technique should enable debugging? Then
the evaluation for the method should be a qualitative study of whether the
method suits model developers and helps them to debug their models. If your
global explanation technique is supposed to infer relevant properties of the
data-generating mechanism, then show in a simulation how well and how resource
efficient your technique approximates these properties. When your local
explanation technique is designed to provide recourse options to end-users,
then either carefully conceptually justify desiderata for recourse and base
your evaluation upon these desiderata, or test the suitability of these
recourse options in experiments.

The purpose determines the right evaluation metric; the evaluation metric(s)
often allows for benchmarking. Different explanation techniques that are
designed for the same purpose can be judged by the same evaluation metric(s)
and thus benchmarked. One simple example is when two methods are estimating
the same quantity i.e. a quantifiable property of the model.

### Step 2: Be Clear What You Need to Explain and by What

Every explanation comes with an explanation target, the so-called
_explanandum_. The explanandum specifies what is to be explained and is
determined by the explanation’s purpose. Very often, confusion in XAI research
arises because it is unclear what the explanandum is in a given context. For
instance, confusions about the right sampling technique are often implicit
confusions about the right explanandum [45, 12]. XAI techniques may for
instance aim to explain:

  * •

the model prediction $\hat{Y}$,

  * •

the predicted target $Y$, or

  * •

an intermediate model element.

If you are clear about the explanandum, the second big question is by what you
want to explain it – the so-called _explanans_. The explanans describes the
factor(s) you are pointing to in order to account for the state of the
explanandum. There are a variety of explanantia (plural of explanans) in XAI
research such as:

  * •

the model inputs $\overline{X}$,

  * •

the predictors $X$,

  * •

the dataset or a subset of it, or

  * •

intermediate model elements.

Finally, be clear on the connection between the explanans and the explanandum.
Explanations can be established by pointing to associations between the
explanans and the explanandum [38]. Usually, however, the relationship we are
interested in is causal, that is, the explanans makes a difference for the
explanandum [46]. While causal explanations are more desirable than reference
to mere associations, they are also more difficult to establish.

### Step 3: Give Clear Instructions for How to Interpret Explanation
Techniques

Interpreting the outputs of XAI techniques is extremely difficult. Rather than
letting people figure out how to interpret XAI statements on their own, papers
should provide clear guidance on how to do so. We believe that addressing the
following questions in new proposals for XAI techniques would contribute to
securing good usage:

  * •

What purpose does this XAI technique serve and how should it be applied?

  * •

Under which (model) conditions does the XAI technique enable a clear
interpretation?

  * •

How do the hyperparameters of the technique affect the interpretation?

  * •

What is the intuitive meaning of extremes, namely high, close to zero, or
negative values?

  * •

In what way, does the explanation guide actions and decisions?

  * •

When is it better to rely on other explanation techniques and why?

### Step 4: XAI Needs Interdisciplinarity AND Expertise

XAI is a highly interdisciplinary field. XAI involves so many aspects that a
single field would fail terribly; we need interaction. XAI needs to solve the
following key questions, among others:

  * •

Conceptual: What are relevant explanation purposes? What is required to
establish an explanatory relationship between an explanans and an explanandum?
What are general explanation desiderata for a specific purpose? How can
explanations be conceptualized? How to interpret explanations?

  * •

Technical: How to describe the conceptual definitions formally? What can be
shown formally about the properties of these explanations? How to compute the
explanations efficiently? How to implement explanations accessibly and
correctly? How to interpret formalized explanations?

  * •

Psychological: How to visualize explanations the right way? What makes a good
explanation for a particular explainee? What are context and person-specific
desiderata of explanations? What cognitive biases do people have when
interpreting explanations? Is the explanation successful in serving the
explanation purpose?

  * •

Social and Ethical: Should we provide explanations and if yes, what are
ethical desiderata? What are the risks with XAI in high-stakes decisions? How
do explanations affect people’s trust and actions? What level of transparency
do we need?

Not every paper must involve researchers from each group. However, the
questions between the different categories should be seen as closely tied:
Formal XAI methods without a conceptual foundation should be disregarded;
Conceptually solid XAI tools that experimentally fail in guiding humans should
be modified and fine-tuned; Finally, XAI explanations that serve a purpose
successfully but this purpose is morally questionable should be dismissed.

At the same time, nothing is wrong with XAI research that focuses on a narrow
field-specific question such as providing a more efficient algorithm or
testing a specific XAI method in human experiments concerning its success in
finding model flaws. Every field has its expertise and it is important that
conceptual foundations, algorithms, experiments, and ethical evaluations live
up to the highest standards of the individual fields. All we want to emphasize
is to not run around having blenders on but recognize how the questions are
intertwined.

##  5 Conclusion

This paper covered the key misconceptions in current XAI research. In our
opinion, the most important one is the idea of purpose-free explanations.
Fixing specific purposes will provide a way for evaluating and benchmarking
XAI techniques objectively. The explanation purpose will also guide us: how
XAI techniques must be constructed, when they should be used, and how they
have to be interpreted. Overall, purpose-centered XAI research will help us
make ML systems more transparent. Therefore, we hope that future researchers
will start thinking more about the purpose of explanations before they make
grand proposals for new methods.

## Acknowledgements

This project has been supported by the German Federal Ministry of Education
and Research (BMBF) and the Carl Zeiss Foundation (project on “Certification
and Foundations of Safe Machine Learning Systems in Healthcare”).

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# Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced
Tract Variables

###### Abstract

The performance of deep learning models depends significantly on their
capacity to encode input features efficiently and decode them into meaningful
outputs. Better input and output representation has the potential to boost
models’ performance and generalization. In the context of acoustic-to-
articulatory speech inversion (SI) systems, we study the impact of utilizing
speech representations acquired via self-supervised learning (SSL) models,
such as HuBERT compared to conventional acoustic features. Additionally, we
investigate the incorporation of novel tract variables (TVs) through an
improved geometric transformation model. By combining these two approaches, we
improve the Pearson product moment correlation (PPMC) scores which evaluate
the accuracy of TV estimation of the SI system from 0.7452 to 0.8141, a 6.9%
increase. Our findings underscore the profound influence of rich feature
representations from SSL models, and improved geometric transformations with
target TVs on the enhanced functionality of SI systems.

Index Terms—  self supervised learning, speech inversion, hubert, tract
variables, xrmb

##  1 Introduction

Articulatory data refers to the positions and motion of different articulators
in the vocal tract during speech. This data has shown to be critical in a
number of speech applications, like speech therapy [1], and mental health
assessment [2]. Articulatory data is obtained using different imaging
techniques, like X-ray Microbeam (XRMB) [3], Electromagnetic Articulometry
(EMA) [4] and real-time Magnetic Resonance Imaging (rt-MRI) [5]. However,
these methods are invasive, expensive, and can be dangerous under prolonged
exposure [6]. Acoustic to articulatory Speech Inversion (SI) provides an
alternative method of estimating the articulatory parameters from the acoustic
signal.

Deep Neural Networks (DNNs) have been shown to be effective SI systems [7, 8].
The performance of DNNs can be improved through better input and output
feature space representation, and SI systems are no exception. In our previous
works, we have shown that SI DNN models’ performance can improve through
better input representation through audio data augmentation [7], or
incorporating source features [8].

Self-Supervised Learning (SSL) has been shown to be an effective method of
improving DNN performance through the utilization of unlabeled data in
learning speech representations[9, 10]. These representations have shown to be
effective in Automatic Speech Recognition (ASR) systems [11], speech
separation and enhancement [12]. Recent works have also shown that SSL speech
representation has the capacity to improve the performance of SI models for
EMA data [13] outperforming the conventional acoustic features like Mel-
frequency Cepstral Coefficients (MFCCs). Cho et al. [13] have extensively
evaluated the existing SSL speech representations for the SI task and have
found that HuBERT based SSL speech representations [14] works the best over
both the other SSL features (eg. wav2vec2, tera [15]) and conventional
acoustic features like MFCCs.

Additionally, the analysis and prediction of raw articulatory data can be
challenging. Raw articulatory data is represented in the absolute X-Y
coordinates of different articulators, which is closely linked to the
speaker’s anatomy, leading to inter-speaker variability in pellet positions
for the same sound. For that reason, quantifying vocal tract shape is best
achieved by measuring the location and degree of these constrictions. These
measurements are called Tract Variables (TVs) and can be achieved through
geometric transformations of the raw articulatory parameters [16]. In a recent
previous work, we have presented a novel geometric transformation which
improved the performance of SI systems through better output feature space
representation [17].

In this work, we combine both approaches, by using HuBERT [9] SSL speech
representation to improve the input representation. We also continue our
previous work presented in [17] by proposing a new geometric transformation
that enhances the performance of SI systems further. We show that using better
input and output feature representations lead to better SI performance and
more robust estimated TVs.

We begin by a description of the XRMB dataset in section 2. We describe our
novel TV transformation model in 3 and our experiments with SSL speech
representations in 4. Section 5 outlines the results of our experiments. We
end-up with a conclusion and a discussion on our proposed future work in
section 6.

##  2 Articulatory Dataset

The original University of Wisconsin XRMB database [3], consists of naturally
spoken isolated sentences and short paragraphs gathered from 32 male and 25
female participants. These speech recordings were accompanied by trajectory
data obtained through X-ray microbeam cinematography of the midsagittal plane
of the vocal tract. This cinematography tracked the movement of pellets placed
on various articulators, including the upper lip (UL), lower lip (LL), tongue
tip (T1), tongue blade (T2), tongue dorsum (T3), tongue root (T4), mandible
incisor (MANi), and parasagittally placed mandible molar (MANm).

However, it’s worth noting that some of the articulatory recordings in the
database were flagged as mistracked. After removing these problematic samples,
we were left with a total of 46 speakers (21 males and 25 females) and
approximately 4 hours of speech data. In our recent work [18], we
reconstructed a large portion of the corrupted articulatory recordings. After
adding the aforementioned reconstructed recordings to the original uncorrupted
dataset, we were left with approximately 5.3 hours of speech data.

Fig. 1: Pellet placement and TV definition in the XRMB dataset

##  3 Novel Tract Variable Transformations

As mentioned above, absolute X-Y coordinate representations of articulatory
data is closely linked to speaker anatomy and leads to inter-speaker
variability. To remedy this, the raw articulatory features are transformed
into TVs using a geometric transformation. In this section, we outline a novel
geometric transformation to extract TVs that are more closely related to the
acoustic signal, which is a continuation of our work presented in [17].

###  3.1 Articulatory Model

####  3.1.1 Lips

The lips are modeled using the UL and LL pellets. To describe the degree and
location of lip constriction, we define two TVs, Lip Aperture (LA) and Lip
Protrusion (LP) respectively. LA is defined as the euclidean distance between
UL and LL. Unlike [17], LP is defined as the horizontal offset of LL from the
Y-axis instead of UL, which we empirically show that it leads to better SI
performance. The origin of the X-Y plane is located at the tip of the
maxillary incisors, and the X- axis is defined as the maxillary occlusal
plane.

$LA[n]=||UL[n]-LL[n]||$ (1) $LP[n]=LL_{x}[n]$ (2)

####  3.1.2 Tongue Body

The tongue body is modeled using a circle fitted through T2, T3 and T4. It’s
constriction can be described using two TVs, namely Tongue Body Constriction
Location (TBCL) and Tongue Body Constriction Degree (TBCD). The constriction
is measured relative to the extended palatal trace we introduced in [17],
which models the hard palate as well as the soft palate and the anterior
pharyngeal wall. Figure 2 shows the extended palatal trace.

Fig. 2:  Extended Palateal Trace With the Anterior Pharyn- geal Wall For
Speaker JW33

TBCD is measured as the minimum Euclidean distance between the tongue body
circle and the extended palatal trace. We update the definition of TBCL from
the one introduced in [17] to be similar to the definition of LP. TBCL is
defined as the horizontal offset of the point on the tongue body circle
closest to the extended palatal trace, i.e. the point used in TBCD calculation
from the Y-axis.

$TBCD=min_{p\in epal}[min_{x\in TB_{circle}}||p-x||]$ (3)
$TBCL=-TB[argmin[TBCD]_{x}]$ (4)

where $epal$ is the extended palate trace, and $TB[argmin[TBCD]]$ is the point
on the tongue body closest to the palate trace

####  3.1.3 Tongue Tip

The tongue tip is modeled by the T1 pallet. It’s constriction can be described
by two TVs, Tongue Tip Constriction Location (TTCL) and Tongue Tip
Constriction Degree (TTCD). Similar to TBCD and TBCL, TTCD is defined as the
minimum Euclidean distance between T1 and the extended pallatal trace, and
TTCL is the horizontal offset of T1 from the Y-axis.

$TTCD=min_{p\in epal}[||p-T1||]$ (5) $TBCL=-T1_{x}$ (6)

Table 1: PPMC between predicted and ground truth TVs for SI systems trained on
datasets according to each geometric transformation model, with the MFCCs and
HuBERT input features.

| Transformation
---
Model
Training Dataset | LA | LP | TBCL | TBCD | TTCL | TTCD | Average
MFCC Input Features
Baseline | Small Dataset | 0.8679 | 0.5902 | 0.7424 | 0.7801 | 0.5971 | 0.8934 | 0.7452
Proposed | Small Dataset | 0.8603 | 0.7104 | 0.7426 | 0.7754 | 0.7422 | 0.8981 | 0.7881
Proposed | Extended Dataset | 0.8697 | 0.7250 | 0.7508 | 0.7847 | 0.7407 | 0.9019 | 0.7955
HuBERT Input Features
Proposed | Small Dataset | 0.8779 | 0.7243 | 0.7430 | 0.8089 | 0.7865 | 0.9248 | 0.8109
Proposed | Extended Dataset | 0.8902 | 0.7142 | 0.7361 | 0.8180 | 0.8032 | 0.9229 | 0.8141

##  4 Speech Inversion Model Architectures

This section describes the experimented SI model architectures and details on
model training.

###  4.1 SI Architecture with HuBERT features

SSL speech representations when used in the SI task with EMA data have shown
to outperform the conventional acoustic features (eg. Mel-spectrograms, Mel-
frequency Cepstral Coefficients (MFCCs)) [13]. Here the SSL representations
only need to be fine-tuned for the down stream task of speech inversion and
can be expected to generalize better even with limited ground-truth
articulatory data. Based on the previous work in [13] for using SSL features
for the SI task with EMA data, we explored the idea of using HuBERT SSL
features [14] as the input acoustic representation to train our best
performing Bidirectional Gated Recurrent Unit (BiGRNN) SI architecture.

We used the HuBERT-large model pre-trained with the Librilight dataset
(60,000h) to extract the HuBERT speech embeddings. All the audio files
(sampled with 16 KHz) are first segmented to 2 second long segments and the
shorter ones are zero padded at the end. The HuBERT embeddings are then
extracted from the 2 second long segments using the speechbrain open-source AI
toolkit [19]. The HuBERT embeddings are sampled at 50 Hz and have a
dimensionality of 1024.

We used the BiGRNN SI system proposed in [7], and adapted the input layer to
match the input dimensionality of the HuBERT embeddings.

###  4.2 SI Architecture with MFCC features

We trained the same SI system architecture used in [17] which is identical to
that discussed in section 4.1 with the only difference being 13 MFCCs used as
the input acoustic feature. The MFCCs were extracted using a 20ms Hamming
analysis window with a 10ms frame shift. The MFCCs are also utterance wise
normalized (z-normalized) prior to model training.

###  4.3 Model Training

Both the SI architectures described above were trained in similar fashion. The
input XRMB dataset was first divided into training, development, and testing
sets, so that the training set has utterances from 36 speakers and the
development and testing sets have 5 speakers each (3 males, 2 females). None
of the training, development and testing sets have overlapping speakers and
hence all the models were trained in a ‘speaker-independent’ fashion. All the
models were implemented with a TensorFlow-Keras machine learning framework.
ADAM optimizer with a starting learning rate of 1e-3 and an exponential
learning rate scheduler was used. Both the models with HuBERT and MFCCs were
trained with an early stopping criteria (patience=5) monitoring the
‘validation loss’ on the development set. To choose the best starting
‘learning rate’, we did a grid search on [1e-3, 3e-4, 1e-4], whereas to choose
the training batch size, we did a similar grid search on [16,32,64,128]. Based
on the validation loss, 1e-3 and 32 were chosen as the learning rate and batch
size, respectively, for the model with HuBERT features and 1e-3 and 64 for the
model with MFCCs.

##  5 Results

###  5.1 TV Transformations

In this subsection, we evaluate our new transformation model and compare it to
the baseline, which is our previous model introduced in [17]. We evaluate the
Transformation models by training the same DNN SI model on the same dataset
not including any reconstructed files, transformed, which we call the ‘small
dataset’, according to each respective geometric transformation. We also
evaluate the SI model when trained on the entire available training data
including reconstructed files, which we call the ‘extended dataset’. We argue
that the better the SI performance, the more closely related the resulting TVs
are to the acoustic input signal. We evaluate the SI model based on the
Pearson Product Moment Correlation (PPMC) between the predicted and ground
truth TVs.

The first part of Table 1 shows the performance of the SI with MFCC input
features. Our proposed model outperforms the baseline on average, by 4.3% on
the small dataset, with noticeable improvement in LP and TTCL. Training on the
extended dataset also improves performance across the board over the small
dataset. Overall, the combination of better transformation and more data has
improved the performance of the SI system by 5.03%.

However, it is worth noting that improving the TV geometric transformation
model was more effective than increasing the size of the training data. This
highlights the importance of having better output feature space
representation.

###  5.2 SSL features with new TV transformations

In this subsection, we discuss the effect of using HuBERT speech
representation as in input to the SI system juxtaposed with MFCCs. The two
model architectures are discussed in section 4.1 and section 4.2.

Training on the small dataset, HuBERT representation lead to a tangible
improvement in the tongue TVs, namely TBCL, TBCD, TTCL and TTCD, with slight
improvement in LA and LP. On average, using HuBERT representations lead to a
2.3% improvement in PPMC scores.

Training on a extended dataset leads to some improvement, although not
significant when compared to improving input representations. On average
adding more training data increases PPMC by 0.32%. This again highlights the
effect of input representation, which was more effective than increasing the
training data size. Overall, by combining better input and output
representations, along with including more data, we were able to improve the
PPMC score by from 0.7452 to 0.8141, a 6.9% improvement.

###  5.3 Estimated TVs with best performing SI systems

Figure 3 shows the estimated LA and constriction degree TVs for an utterance
in the test set, by the two SI systems trained with HuBERT and MFCC features.
Both the systems have been trained with the ‘extended dataset’. As seen in the
figure, the differences between the TV estimates by the two models are subtle.
But consistent with the PPMC scores in Table 1, it can be seen that both LA
and TTCD TVs are estimated better with the HuBERT based model compared to the
model trained with MFCCs. It can also be seen that the TBCD is better
estimated by the model trained with MFCCs compared to the HuBERT based model.

Fig. 3: LA and constriction degree TVs for the utterance ‘The dormitory is
between the house and the school’ estimated by the model trained with HuBERT
embeddings (estimated_hubert) and the model trained with MFCCs
(estimated_mfcc). Solid blue Line - ground truth, black dotted line -
predictions by the HuBERT based model, yellow dotted Line - predictions by
MFCC based model.

##  6 Conclusion and Future Work

In this paper, we propose a new geometric transformation to obtain TVs from
raw XRMB pellets. We show that our novel TV transformation improves the
performance of SI model over the baseline, which can be attributed to better
relation between the resulting TVs and the acoustic signal. We also further
improve the performance of the SI system by using HuBERT speech representation
as the input to the SI model. Our findings highlight the importance of
efficient input and output feature space representations.

In [17], we highlighted some of the limitations of the TV transformation model
we proposed in that paper. In this paper, we tackled a majority of these
limitations. However, we still lag behind the transformation proposed in [16]
with respect to TBCL, even though we achieve better PPMC on average. This can
be attributed to the fact that even though we extended the palatal trace
towards the anterior pharyngeal wall, we didn’t extend the tongue body circle
beyond the T4. Even though this model improved the representation of TBCD,
which means it gave a good estimate for the degree of the constriction, not
extending the tongue body circle might lead to inaccurate estimation of the
location of the constriction, which in turn would lead to lower correlation
between TTCL and the acoustic signal. We intend to tackle this problem in
future work.

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