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replace=] [fieldsource=location, match=.+, replace=] [fieldsource=address,
match=.+, replace=] [fieldsource=organization, match=.+, replace=] Institute
for Natural Language Processing (IMS), University of Stuttgart, Germany

# Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-
Sequence End-to-End Spoken Language Understanding

###### Abstract

A number of methods have been proposed for End-to-End Spoken Language
Understanding (E2E-SLU) using pretrained models, however their evaluation
often lacks multilingual setup and tasks that require prediction of lexical
fillers, such as slot filling. In this work, we propose a unified method that
integrates multilingual pretrained speech and text models and performs E2E-SLU
on six datasets in four languages in a generative manner, including the
prediction of lexical fillers. We investigate how the proposed method can be
improved by pretraining on widely available speech recognition data using
several training objectives. Pretraining on 7000 hours of multilingual data
allows us to outperform the state-of-the-art ultimately on two SLU datasets
and partly on two more SLU datasets. Finally, we examine the cross-lingual
capabilities of the proposed model and improve on the best known result on the
PortMEDIA-Language dataset by almost half, achieving a Concept/Value Error
Rate of 23.65%.

Index Terms—  spoken language understanding, self-supervised learning, end-to-
end, sequence-to-sequence, multilingual

##  1 Introduction

Spoken Language Understanding (SLU) is a common name for tasks combining
speech and language processing to extract semantic concepts from spoken
sentences, such as intents, slots, and named entities. This functionality is
essential to various systems with a voice interface, including intelligent
assistants and automatic call answering services. Traditionally, SLU has been
decomposed to Automatic Speech Recognition (ASR) and Natural Language
Understanding (NLU) subtasks that are solved sequentially in a pipeline
manner. In this scenario, ASR converts speech recording to text representation
that is then processed by NLU. The advantage of a pipelined approach is that
both ASR and NLU can be optimized independently using numerous datasets
labelled for the corresponding tasks. The disadvantages are that: first, the
text representation lacks paralinguistic information, such as prosody and
punctuation in most cases, and in addition to that contains errors introduced
by ASR and propagated to NLU; and second, sequential execution of ASR and NLU
introduces a time lag that is not desirable in an interactive context. These
downsides motivated the community to work on the End-to-End (E2E) SLU methods
that allow building a single model performing SLU task directly on speech
input.

One of the critical challenges in E2E SLU is data sparsity since the
availability of labelled datasets for SLU is even lower than for ASR or NLU.
Transfer learning is a popular technique that alleviates the data sparsity
problem for one task by learning from another related task. More recently,
Self-Supervised Learning (SSL) methods demonstrate the possibility of gaining
improvements even without requiring annotation for any task by training on
unlabelled data of corresponding modalities, such as text [1] or speech [2].

Usually, SLU tasks are treated as a classification problem, either on the
sequence level (Intent Classification (IC) [3]) or on the token level (Slot
Filling (SF) [4] and Named Entity Recognition (NER) [5]). Few works, however,
represent SLU tasks as generation problems [6, 7]. There are multiple examples
of classification-based SLU systems utilizing SSL pretrained speech and text
encoders [3, 8]. However, usage of SSL pretrained models in generation-based
SLU systems is limited to speech encoders [9]. Aside from that, generation-
based SLU descriptions are typically focused on one language only [10, 11]. We
aim to close these two gaps by employing multilingual SSL pretrained speech
and text models for solving SLU tasks in multiple languages via sequence-to-
sequence modeling. In this work, we propose a unified architecture for
building E2E SLU models and evaluate it on a diverse set of established SLU
benchmarks. Our experiments demonstrate that multilingual SSL pretrained text-
to-text model can be fine-tuned to solve token level NLU tasks in a generative
way and this can be transferred to speech modality with the help of
multilingual SSL pretrained speech encoder. Furthermore, we improve the NLU to
SLU transferability by aligning the hidden representations of speech and text
using a medium-sized multilingual ASR dataset and different training
approaches: Connectionist Temporal Classification (CTC), Attention Encoder-
Decoder (AED) and a novel Modality Correlation (MC) objective. In several
cases, our results are better than the best previously reported. We provide
the implementation, configurations, data preparation and scoring scripts and
pretrained models at https://github.com/DigitalPhonetics/multilingual-seq2seq-
slu.

Fig. 1:  General architecture of our SLU model. The speech encoder and text
encoder are initialized from unchanged SSL pretrained models. The text decoder
is initialized from an SSL pretrained model that is fine-tuned on the ground
truth transcriptions of an SLU dataset. The Adaptor is trained from scratch on
an SLU dataset or is pretrained on ASR data (section 2.2).

##  2 Method

###  2.1 SLU model

The proposed approach is outlined in Figure 1. Our SLU model combines SSL
pretrained speech encoder and text-to-text encoder-decoder models. The raw
speech input $x$ is processed by the speech encoder that outputs an acoustic
representation $\textbf{H}^{\mathrm{Feat}}$. The Adaptor maps the acoustic
representation $\textbf{H}^{\mathrm{Feat}}$ from the speech encoder to a
quasi-graphemic representation $\textbf{H}^{\mathrm{PreEnc}}$ that resembles
text token embeddings and is fed to the text encoder. Output of the text
encoder $\textbf{H}^{\mathrm{PostEnc}}$ is decoded by the autoregressive text
decoder producing an output hidden representation
$\textbf{H}^{\mathrm{PostDec}}$ that is projected to text token logits used to
produce an output text token sequence $y$. The speech and text encoder layers
are initialized from the parameters of the general pretrained models without
any changes. The text decoder parameters are first fine-tuned on the
corresponding SLU dataset using the ground truth transcriptions as input and
the SLU annotations as output, thus resulting in the text based NLU model. The
text encoder is kept frozen during this step to ensure the transferability to
the SLU model initialized from the general parameters. Only the Adaptor’s
parameters have to be learned during the SLU training, and this motivates us
to investigate the Adaptor pretraining.

(a) PreEnc CTC Adaptor pretraining.

(b) PreEnc MC Adaptor pretraining.

(c) PostEnc MC Adaptor pretraining.

(d) PostDec MC Adaptor pretraining.

(e) PostEnc AED Adaptor pretraining.

Fig. 2:  Investigated options for the Adaptor pretraining using pairs of
speech recordings and text transcriptions. The Adaptor is optimized to predict
the hidden representation of the speech that is as close to the hidden
representation of the text as possible. Each Adaptor pretraining option refers
to a combination of the hidden representation layer (PreEnc, PostEnc, PostDec)
and the training method (MC, CTC, AED). The MC loss can be applied directly to
the hidden representations of the speech and the text before the text encoder
(b), after the text encoder (c) or after the text decoder (d). Alternatively,
the hidden speech representation can be projected to the token logits via a
regular linear transformation (Token Projection) allowing CTC training if the
hidden speech representation is extracted before the text encoder (a) or AED
training if the hidden speech representation is extracted after the text
decoder (e).

###  2.2 Adaptor pretraining

Generally, the Adaptor pretraining aims to minimize the distance between the
speech representation in the SLU model and the text representation in the SSL
text model. As Figure 2 shows, this can be done on multiple levels of the
neural network using various training strategies. We select three types of a
text representation and the corresponding levels of the SLU model to extract a
logically similar speech representation:  (i) text token embeddings
$\textbf{H}^{\mathrm{PreEnc}}_{\mathrm{Text}}$ and the SLU Adaptor output
$\textbf{H}^{\mathrm{PreEnc}}_{\mathrm{Speech}}$;  (ii) the hidden text
representation $\textbf{H}^{\mathrm{PostEnc}}_{\mathrm{Text}}$ after the
encoder of the SSL text model and the hidden speech representation
$\textbf{H}^{\mathrm{PostEnc}}_{\mathrm{Speech}}$ after the text encoder of
the SLU model;  (iii) the output text representation
$\textbf{H}^{\mathrm{PostDec}}_{\mathrm{Text}}$ after the decoder of the SSL
text model and the hidden speech representation
$\textbf{H}^{\mathrm{PostDec}}_{\mathrm{Speech}}$ after the text decoder of
the SLU model.

The Adaptor output can be aligned with the text token embeddings using the CTC
loss function in the vein of LegoNN approach [12]. While this is the most
straightforward and computationally cheap way to pretrain Adaptor parameters
in our framework, it might set an unnecessary strict target for the alignment
because the text-to-text network has some level of robustness to noisy inputs
and not all differences between text and speech representations are equally
harmful for the prediction of the correct output.

Given this consideration, it might be better to pretrain the Adaptor in the
full AED model while keeping all parameters except of the Adaptor in a frozen
state. This way, the output of the decoder serves as a source of training
signal and the Adaptor parameters receive only the relevant updates for the
prediction of the correct output. The disadvantage of this approach is that
the differences between the original decoder and a task specific NLU decoder
used in a SLU model can hinder the transferability of the pretrained Adaptor
output. It is fair to assume that the practical importance of this problem
depends on the actual differences between the original and task specific
decoders. These differences can be avoided by the parameter efficient tuning
of the decoder [13].

Finally, any hidden speech representation can be trained directly on a hidden
text representation as a target. In order to do that, we propose the modality
matching approach. Its design is inspired by the cross-modal grounding methods
[14, 15] and the Barlow twins loss [16]. First, we construct a speech-text
correlation matrix:

$\textbf{C}_{\mathrm{Speech-
Text}}=\textbf{H}_{\mathrm{Speech}}{(\textbf{H}_{\mathrm{Text}})}^{\intercal},$

where $\textbf{H}_{\text{Speech}}\in\mathbb{R}^{L_{\mathrm{max}}\times
d_{\mathrm{model}}}$ and
$\textbf{H}_{\text{Text}}\in\mathbb{R}^{L_{\mathrm{max}}\times
d_{\mathrm{model}}}$ are padded normalized hidden speech and text
representations, $L_{\mathrm{max}}=max(L_{\mathrm{Speech}},L_{\mathrm{Text}})$
is the maximum length of the speech and text sequences and
$d_{\mathrm{model}}$ is the hidden representation dimension in both
modalities. The representations are zero-padded and normalized to Euclidean
norm along each hidden representation vector. We mask out the loss for
elements outside of $L_{\mathrm{Text}}$, because the zero-dominated matrices
caused convergence problems. This can be reduced to the truncation instead of
the padding and masking, but we implement it that way because of the batching.
A ground truth for $\textbf{C}_{\mathrm{Speech-Text}}$ is set to be analogous
text-text self-correlation matrix:

$\textbf{C}_{\mathrm{Text-
Text}}=\textbf{H}_{\mathrm{Text}}{(\textbf{H}_{\mathrm{Text}})}^{\intercal}$

During the training, we minimize the mean square error between
$\textbf{C}_{\mathrm{Speech-Text}}$ and $\textbf{C}_{\mathrm{Text-Text}}$. The
MC approach can be applied to the encoder outputs and therefore allows a
trade-off between the two previously mentioned levels of the neural network.
In addition, it can be employed as an alternative to the hard label based CTC
and AED approaches applicable only at the specific levels of the neural
network.

Table 1: SLU benchmarks used for the evaluation. #Concepts denotes number of unique slot labels for SF or unique entity labels for NER. | SLURP | SLUE | CATSLU | MEDIA | PM-Dom | PM-Lang
---|---|---|---|---|---|---
Samples (train/ dev/test) | 120K/ | 5.0K/ | 7.0K/ | 12.0K | 5.8K/ | 6.0K/
8.6K/ | 1.7K/ | 1.6K/ | 1.2K/ | 1.3K/ | 1.9K/
13.0K | 1.8K | 5.8K | 3.5K | 2.8K | 3.8K
Hours (train/ dev/test) | 84.7/ | 14.0/ | 5.5/ | 16.1/ | 7.4/ | 7.5/
6.9/ | 4.9/ | 1.3/ | 1.6/ | 1.6/ | 2.5/
10.3 | 4.9 | 4.8 | 4.8 | 3.4 | 5.0
Language | English | English | Mandarin | French | French | Italian
Tasks | IC, SF | NER | SF | SF | SF | SF
#Concepts | 56 | 7 | 54 | 143 | 34 | 124

##  3 Experimental setup

###  3.1 Data

We assess the performance of the proposed method using the six established SLU
benchmarks: SLURP [4], SLUE-VoxPopuli [5] (evaluating on the validation set),
CATSLU [17], MEDIA [18], PortMEDIA-Domain and PortMEDIA-Language [19]. These
datasets cover three SLU tasks in four languages belonging to three families
and have medium to low resource data regimes. Details of the SLU datasets are
given in Table 1. In addition to SF annotation, each utterance of SLURP is
labeled with one out of 59 unique intents. We evaluate the SLU systems using
the established metrics for each dataset: IC accuracy and SLU-F1 for SLURP, F1
and label-F1 for SLUE-VoxPopuli, accuracy and F1 for CATSLU, Concept Error
Rate (CER) and Concept/Value Error Rate (CVER) for MEDIA, PortMEDIA-Domain and
PortMEDIA-Language datasets. The SLU-F1 combines the slot label F1 with the
word and character edit distances of the slot value [4]. The CER and CVER are
based on the edit distance and are calculated the same way as the word error
rate, but on the sequences of slot labels or slot labels combined with the
values instead of words.

The Adaptor pretraining experiments are performed using subsets of Common
Voice Corpus 9.0 [20] and WenetSpeech [21]. We select the 36 languages that
are present in both XLS-R [22] and mBART50 [23] pretraining data and sample
down the resulting training and validation subsets uniformly to 1000 and 20
hours respectively.

###  3.2 Training details

NLU models are obtained by fine-tuning the SSL text-to-text model on the
ground truth transcriptions of each SLU dataset and its task specific outputs.
After preliminary experiments the mBART50 Large model [23] was chosen because
of its best scores. According to the original mBART50 approach, the language
is encoded as a special token that is added at the beginning of the input
sequence and is given as the initial output token to the decoder. Parameters
of the model’s encoder and token embeddings are frozen.

SLU models are implemented in ESPnet-SLU toolkit [9] and follow its SLURP
recipe. We use the weighted-sum of hidden states [24, 25] of XLS-R (0.3B)
pretrained model [22] as speech features. The Adaptor module is technically
organized as a VGG/Conformer based encoder [26, 27] followed by a
convolutional Length Adaptor [28]. This design is based on the encoder
architecture of ASR [25] and SLU [9] systems and demonstrated better results
in our preliminary experiments compared to the direct fine-tuning of SSL
speech encoders. Output of the Adaptor module is fed to the general mBART50
text encoder that is followed by the fine-tuned text decoder from the NLU
model. As in NLU, both the text encoder and decoder are conditioned on the SLU
dataset language by adding the special language token embedding at the
beginning of the text encoder’s input sequence and by setting the special
language token as the initial output of the text decoder. Conformer layers are
configured with $d_{\mathrm{model}}=1024$, $d_{\mathrm{ff}}=4096$, $d_{h}=8$,
$E=8$ and $\mathrm{Conv}$ kernel size of 31. The Length Adaptor contains a
1-dimensional convolutional layer with stride 2 and reduces the length of
input sequence by factor of 2. Label smoothing with a penalty of 0.1, as well
as 3-way speed perturbation [29] data augmentation method are utilized during
the training. The training is done with Adam optimizer [30] with
$\beta_{1}=0.9$, $\beta_{2}=0.999$, $\epsilon=10^{-8}$ and warmup learning
rate scheduler. Number of epochs and warmup steps, maximum learning rate and
batch size are tuned individually for each SLU dataset.

SLU Adaptor pretraining is carried out with the configuration similar to the
SLU model training. We set the maximum learning rate to 5e-5, the number of
warmup steps to 25k and the number of epochs to 30 with the early stopping
after 3 epochs of no improvements in validation accuracy.

Table 2:  Results of the NLU models on the ground truth transcriptions and the SLU models on the speech recordings (without Adaptor pretraining).  Dataset | Metrics | NLU | SLU
---|---|---|---
SLURP |  IC Acc.$\uparrow$ | 85.67 | 86.97
SLU-F1$\uparrow$ | 79.30 | 77.71
SLUE |  F1$\uparrow$ | 83.25 | 68.90
label-F1$\uparrow$ | 87.76 | 82.28
CATSLU |  Acc.$\uparrow$ | 82.56 | 63.87
F1$\uparrow$ | 73.48 | 48.33
MEDIA |  CER$\downarrow$ | 16.50 | 13.67
CVER$\downarrow$ | 19.09 | 16.28
PM-Dom |  CER$\downarrow$ | 23.49 | 21.43
CVER$\downarrow$ | 26.59 | 24.62
PM-Lang |  CER$\downarrow$ | 40.76 | 25.13
CVER$\downarrow$ | 43.93 | 29.11

##  4 Results

###  4.1 Baseline

Table 2 shows the results of the NLU models on the ground truth transcriptions
and the baseline SLU results on the speech recordings. Each baseline SLU
system is essentially the corresponding NLU model transferred to the speech
modality using the SSL pretrained speech encoder as described in the section
2.1. The Adaptor’s parameters in the baseline SLU systems have to be trained
from scratch using the speech recordings from the SLU datasets only. First of
all, we note that the SSL text-to-text model can in principle be fine-tuned to
perform various NLU tasks in a generative manner. Next, we observe that the
gap between the NLU and SLU performance correlates with the amount of training
data for the English benchmarks, which suggests that the low resource setting
is an issue for our initial SLU model. On the other side, SLU performance is
slightly better than the NLU performance for the French benchmarks and is much
better for the Italian benchmark, despite the small amount of training data.
This might be due to the encoder or token embeddings freeze during the NLU
training: both French and Italian likely have smaller presence in the
pretraining data of the original mBART50 model. Finally, the Mandarin
benchmark demonstrates the large gap between the NLU and SLU performance, what
we explain by the very small presence of Mandarin and related languages in the
XLS-R pretraining.

###  4.2 Adaptor pretraininig

Table 3:  Results depending on the layer and loss function for Adaptor pretraining and the average Relative Error Reduction (RER) compared to the error rate without Adaptor pretraining. The error rate is calculated by subtracting from 100 for the accuracy, SLU-F1, F1 and label-F1 metrics. The last two lines contain the results for the combined losses. Underlined numbers show the best scores for the individual losses (without combination). Bold numbers show the best overall scores.  ID | Adaptor | SLURP | SLUE | CATSLU | MEDIA | PM-Dom | PM-Lang | Average
---|---|---|---|---|---|---|---|---
| pretraining |  IC Acc.$\uparrow$ | SLU-F1$\uparrow$ | F1$\uparrow$ | label-F1$\uparrow$ | Acc.$\uparrow$ | F1$\uparrow$ | CER$\downarrow$ | CVER$\downarrow$ | CER$\downarrow$ | CVER$\downarrow$ | CER$\downarrow$ | CVER$\downarrow$ | RER, %
0 | None | 86.97 | 77.71 | 68.90 | 82.28 | 48.33 | 63.87 | 13.67 | 16.28 | 21.43 | 24.62 | 25.13 | 29.11 | -
1 | PreEnc MC | 87.79 | 77.85 | 69.60 | 83.60 | 43.63 | 59.02 | 13.65 | 16.66 | 20.51 | 24.02 | 26.49 | 30.62 | -3.15
2 | PreEnc CTC | 88.59 | 78.80 | 71.25 | 84.04 | 50.33 | 65.73 | 12.44 | 15.13 | 19.54 | 23.06 | 22.93 | 26.50 | 6.81
3 | PostEnc MC | 88.13 | 78.91 | 71.93 | 85.10 | 47.50 | 62.65 | 12.31 | 15.07 | 19.67 | 22.81 | 23.36 | 27.22 | 5.12
4 | PostDec MC | 88.93 | 79.22 | 72.95 | 86.56 | 50.52 | 65.84 | 12.60 | 15.25 | 17.90 | 21.15 | 22.52 | 26.06 | 9.95
5 | PostDec AED | 89.33 | 80.08 | 72.94 | 86.57 | 54.54 | 69.24 | 13.64 | 16.18 | 18.00 | 21.27 | 21.88 | 25.04 | 12.63
6 | 2 + 5 | 89.14 | 80.07 | 73.07 | 86.05 | 53.63 | 68.37 | 12.12 | 14.71 | 19.38 | 22.36 | 23.28 | 26.78 | 10.97
7 | 2 + 3 + 5 | 89.20 | 79.58 | 73.05 | 86.20 | 50.81 | 65.55 | 12.11 | 14.83 | 18.85 | 21.91 | 23.40 | 26.72 | 9.17

A comparison of the Adaptor pretraining methods is given in Table 3. It is
evident from these results that both medium and low resource benchmarks
benefit from the Adaptor pretraining in general. Hard label loss appears to
yield better performance than the MC for all datasets on the PreEnc level and
for three out of six datasets on the PostDec level. Interestingly, the PostDec
level provides the best results on all datasets except MEDIA. Four out of
these datasets contain the smallest amount of training sentences, therefore
the changes in the decoder during the NLU fine-tuning are probably
sufficiently small so that it is compatible with the Adaptor pretrained with
the general mBART50 decoder. SLURP dataset also works best with the PostDec
AED pretrained Adaptor despite the largest amount of training sentences.
Unlike the rest of the datasets, we observe the best scores on MEDIA for the
PostEnc MC aligned Adaptor, possibly because the NLU fine-tuning on this task
leads to the larger modifications in the mBART50 decoder, therefore reducing
its compatibility with the general decoder. Visualization of the parameter
differences in Figure 3 shows that the relative change lies in the higher
range for the higher number of parameters after fine-tuning on MEDIA dataset
compared to the other tasks. Additionally, we calculate the ratio of unique
tokens from each SLU dataset that is seen during the pretraining, as shown in
Table 4. It can be observed from these numbers that the AED method is more
effective for the datasets with at least 95% unique test set tokens seen
during the pretraining and the MC loss is more effective otherwise. The MEDIA
dataset again stands out in this analysis with the lowest ratio of unique
training set tokens seen in the pretraining, this might also explain the lower
PostDec pretraining effectiveness.

After exploring the training approaches separately, we try to combine them by
simply summing the loss outputs. No systems with the multitask pretrained
Adaptor degrade considerably compared to the best system for each dataset,
which indicates that different pretraining objectives regularize each other
when combined. Although the multitask pretraining does not outperform any
single objective pretraining, the combination of CTC and AED can be
recommended as a trade-off solution when little is known about the SLU data.

Fig. 3:  Distribution of the differences between the parameters of the original mBART50 decoder and the fine-tuned NLU decoders depending on the dataset. The difference is computed as a percentage of the original parameter value. A wider line indicates a larger number of parameters having certain difference.  Table 4:  Influence of the SLU benchmark’s unique tokens seen in the Adaptor pretraining data on the best pretraining method for that SLU benchmark.  Dataset | Tokens seen in pretraining, % | Best pretraining
---|---|---
|  Train |  Test | method
SLURP |  98.45 |  98.93 | PostDec AED
SLUE |  98.93 |  99.66 | PostDec MC/AED
CATSLU |  98.93 |  99.41 | PostDec AED
MEDIA |  91.59 |  93.38 | PostEnc MC
PM-Dom |  93.47 |  93.20 | PostDec MC
PM-Lang |  95.52 |  95.67 | PostDec AED

####  4.2.1 Language of pretraining data

In order to examine the importance of the multilingual Adaptor pretraining, we
experiment with the PostEnc MC approach but replace the training data with
1000 hours of English recordings sampled from the Common Voice corpus. The
results are displayed in Table 5. While the differences with the multilingual
pretraining are rather small, the results confirm that the Adaptor pretraining
should at least include the languages corresponding to the downstream SLU
tasks. Moreover, the spoken NER model on the English recordings is also
improved by the multilingual Adaptor pretraining, what can be attributed to
the relatively large amount of the loanwords in this task.

Table 5: Effect of the languages of Adaptor pretraining data (PostEnc MC loss). Dataset | Metrics | Pretraining languages
---|---|---
| English | Multiple
SLURP |  IC Acc.$\uparrow$ | 88.62 | 88.13
SLU-F1$\uparrow$ | 79.09 | 78.91
SLUE |  F1$\uparrow$ | 72.06 | 71.93
label-F1$\uparrow$ | 84.40 | 85.10
CATSLU |  Acc.$\uparrow$ | 47.08 | 47.50
F1$\uparrow$ | 62.43 | 62.65
MEDIA |  CER$\downarrow$ | 12.88 | 12.31
CVER$\downarrow$ | 15.75 | 15.07
PM-Dom |  CER$\downarrow$ | 19.65 | 19.67
CVER$\downarrow$ | 23.55 | 22.81
PM-Lang |  CER$\downarrow$ | 23.71 | 23.36
CVER$\downarrow$ | 27.83 | 27.22

####  4.2.2 Scaling up Adaptor pretraining

We select the best performing configuration (PostDec AED) and run the Adaptor
pretraining on our full ASR dataset comprising of 7000 hours. It can be seen
from Table 6 that the additional pretraining data improves the results in
almost all cases indicating a scaling potential of our method. In order to
compare our E2E SLU with the pipeline approach, we transcribe the SLU datasets
using the PostDec AED model trained on 7000 hours and subsequently fine-tune
and evaluate the NLU models in these ASR transcriptions. As the comparison
with the pipeline results in Table 6 shows, our E2E SLU approach can offer
more accurate predictions given the same training data and pretrained models
as the pipeline. Moreover, Table 6 shows the comparison of our best result
with the previous work. The proposed approach demonstrates overall competitive
results with the exception of CATSLU dataset. Our systems outperform the
previous results on four datasets according to the metrics that evaluate
predictions with variable length and large vocabulary (SLU-F1, CVER), and only
on two datasets if we take into account the metrics that evaluate either a
single element from a small predefined set of values (IC accuracy) or a
sequence of such elements (CER). This suggests that for tasks with a larger
prediction space, the generative capabilities of our model are more important.

Table 6:  Scaling up the Adaptor PostDec AED pretraining with additional data and comparison with the pipeline approach and with the prior work.  Dataset | Metrics | Data, hours | Pipeline | Prior work
---|---|---|---|---
|  | 1K | 7K |  |  |
SLURP |  IC Acc.$\uparrow$ | 89.33 | 90.04 | 64.88 | 90.07 | [31]
SLU-F1$\uparrow$ | 80.08 | 80.66 | 54.78 | 79.90
SLUE |  F1$\uparrow$ | 72.94 | 75.47 | 63.57 | 77.20 | [32]
label-F1$\uparrow$ | 86.57 | 88.14 | 76.66 | 88.70
CATSLU |  Acc.$\uparrow$ | 54.54 | 56.34 | 38.46 | 86.30 | [33]
F1$\uparrow$ | 69.24 | 71.07 | 58.49 | 92.56
MEDIA |  CER$\downarrow$ | 13.64 | 12.07 | 29.51 | 11.20 | [34]
CVER$\downarrow$ | 16.18 | 14.57 | 33.45 | 17.20
PM-Dom |  CER$\downarrow$ | 18.00 | 17.90 | 44.80 | 21.90 | [35]
CVER$\downarrow$ | 21.27 | 21.08 | 51.52 | 35.90
PM-Lang |  CER$\downarrow$ | 21.88 | 21.50 | 49.53 | 26.18 | [35]
CVER$\downarrow$ | 25.04 | 25.04 | 54.62 | 39.28

###  4.3 Cross-lingual SLU

Our SLU approach is mostly multilingual, so the model is highly likely to have
cross-lingual capabilities. The PortMEDIA-Language benchmark is specifically
designed to evaluate this aspect because it uses the same SF tags as the MEDIA
corpus, but is recorded in Italian instead of French. Similarly to [8], we
experiment with our best MEDIA model and evaluate it on PortMEDIA-Language
test set without any modification and after fine-tuning on the PortMEDIA-
Language training set. The results shown in Table 7 outperform by a large
margin most of the zero-shot results reported in Table 3 of [8] and all of the
fine-tuning results reported in Table 5 of [8]. On top of that, our fine-
tuning numbers are better than any previously reported.

Table 7: Results of transfer learning from MEDIA (French) to PM-Lang (Italian). Metrics | No transfer | Zero-shot | Fine-tuning
---|---|---|---
CER$\downarrow$ | 21.50 | 64.93 | 20.30
CVER$\downarrow$ | 25.04 | 71.64 | 23.65

##  5 Conclusions

We propose a unified E2E SLU approach based on the multilingual SSL pretrained
speech and text-to-text models and evaluate it on multiple SLU benchmarks. The
evaluation results are comparable to or better than the previously reported,
but apply to a more diverse set of the SLU benchmarks. The pretraining on the
medium amount of ASR data using the popular CTC and AED and the novel MC
approaches helps to improve the scores across the board and allows
outperforming the best-known configurations in several cases, suggesting that
the proposed model can be improved further.

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