# Estimation of speaker age and height from speech signal using bi-encoder
transformer mixture model

###### Abstract

The estimation of speaker characteristics such as age and height is a
challenging task, having numerous applications in voice forensic analysis. In
this work, we propose a bi-encoder transformer mixture model for speaker age
and height estimation. Considering the wide differences in male and female
voice characteristics such as differences in formant and fundamental
frequencies, we propose the use of two separate transformer encoders for the
extraction of specific voice features in the male and female gender, using
wav2vec 2.0 as a common-level feature extractor. This architecture reduces the
interference effects during backpropagation and improves the generalizability
of the model. We perform our experiments on the TIMIT dataset and
significantly outperform the current state-of-the-art results on age
estimation. Specifically, we achieve root mean squared error (RMSE) of 5.54
years and 6.49 years for male and female age estimation, respectively. Further
experiment to evaluate the relative importance of different phonetic types for
our task demonstrate that vowel sounds are the most distinguishing for age
estimation.111Code and models are available at
github/tarun360/SpeakerProfiling

Index Terms: speaker profiling, age estimation, height estimation, mixture of
experts.

##  1 Introduction

Speech is an acoustic output produced by precisely coordinated movement of
different human body parts. Therefore, there have been suggestions that
acoustic features of speech can convey information about the physical
characteristics of the speaker. Among various physical characteristics,
scientific studies have investigated the correlation between voice
characteristics and a speaker’s age and height. Authors of [1, 2] reported
that the vocal tract length, sub-glottal resonance frequencies, and formant
frequencies are correlated with the individual’s height. Other voice
characteristics of speech such as speech rate, sound pressure level,
fundamental frequency, etc. vary according to the speaker’s age [2, 3]. The
age-related glottis deterioration of the speaker also impacts the speech
characteristics like jitter, shimmer [4], and speech harmonics [5].

Automatic speaker profiling systems could be applied to a variety of different
fields. For example, in criminal investigation, evidence could be in the form
of voice recordings, e.g. a hoax bomb threat or ransom demand over a phone
call [6, 7]. In such cases, estimating the age and height of speakers in the
audio evidence could save investigating agencies’ time by narrowing down the
number of suspects. Similarly, predicting the age and gender of a speaker from
the speech data can aid marketing campaigns by targeting suitable gender/age
customer groups [7]. In addition, the speaker profiling system can benefit
other speech fields, namely, speaker diarization and speech-based verification
as well.

Contribution: In this work, we study the use of self-supervised speech
representation, specifically wav2vec 2.0 [8], for speaker age and height
estimation. We also compare wav2vec 2.0 to other famous feature
representations: MFCC, filter bank, and x-vectors [9]. To our best knowledge,
our work is the first in the literature to demonstrate the potential of
wav2vec 2.0 in predicting speaker age and height. For the downstream
architecture, we present a novel Mixture of Experts (MoE) based bi-encoder
transformer model that utilizes wav2vec 2.0 as a common-level speech feature
extractor followed by a bi-encoder architecture. The bi-encoder architecture
is motivated from the differences in male and female voice characteristics
such as differences in formant and fundamental frequencies [10, 11]. The two
encoders act as ‘experts’ for providing distinctive features for male and
female voice. We further make use of homoscedastic uncertainty [12] to define
the multi-task loss function and mixup [13] as a regularization technique. The
proposed network achieve new state-of-the-art results in age estimation on the
TIMIT dataset.

The rest of our paper is organized as follows. Section 2 briefly describes the
speaker profiling literature. Section 3 illustrates our proposed model
architecture and techniques. Section 4 describes the experiments conducted to
estimate the age and height of a speaker in a monolingual setting using the
TIMIT dataset. Also, this section discusses the impact of the different phones
on the estimation result in Section 4.3. Finally, conclusions are presented in
Section 5.

Figure 1: wav2vec 2.0 bi-encoder model architecture. Transformer encoder [14]
with 6 layers and 8 attention heads is used. Statistical pooling refers to
taking mean and standard deviation along frame dimension and concatenating
them to obtain utterance level representation. ‘FC-L’ denotes a fully
connected layer with L neurons. $\oplus$ denotes the concatenation operation.
a, h and g denote the age, height and gender prediction.

##  2 Related works

There is a wide choice of speech feature extractors for automatic height and
age estimation available in the literature. However, most of the previous
studies used conventional techniques of extracting features from raw speech
signals. In the earlier years, authors of [15, 16] used the Open-Smile toolkit
to convert the short-term spectral features into various statistics like mean,
median, percentiles, etc. for height and age estimation. A similar statistical
approach for age and height prediction applied i-vector (dimension reduced
version of Gaussian mixture model universal background model) [17, 18] to
convert a variable-length utterance into a fixed-size embedding vector.
Another embedding approach of speaker profiling is obtaining spectral
characterizations of the speech signal. For example, the speaker’s resonance
frequencies of sub-glottal are used for height estimation [19]. Singh et al.
[2] applied a bag of words representation to capture short-term cepstral
features with different time resolutions. Other common-level short term
features are Mel Frequency Cepstral Coefficients (MFCC) [20, 21], cepstral and
pitch features [4, 22], etc.

In this era of deep learning, deep neural networks (DNN) have shown the
outstanding ability to discover descriptive and distinctive representations
from raw speech audio. Therefore, recent studies have applied DNN-based speech
representation learning to improve the accuracy of the speaker’s height and
age prediction. Abumallouh et al. [23] presented a speaker age and gender
classifier that based on top of an unsupervised DNN bottleneck feature
extractor can achieve better performance than its original MFCCs feature set,
especially for female speaker. By applying DNN discriminative embedding called
x-vector [24], authors of [25, 26] gained better results in height and age
estimation in TIMIT dataset compared to previous studies. Shangeth et al. [27]
employed semi-supervised learning approach to learn speaker representation and
achieve the state-of-the-art result on age estimation on the TIMIT Test
dataset with Mean Absolute Error (MAE) of 4.8 and 5.0 years for male and
female speakers respectively. Recently, Baevski et al. [8] presented the
wav2vec 2.0 framework for self-supervised learning of discrete speech units.
In the field of speaker recognition, wav2vec 2.0 have increased the
classification accuracy considerably since it can capture much more phonetics
information than it’s conventional counterpart. To the best of our knowledge,
there is no work in the literature which studies the use of wav2vec 2.0 for
speaker height and age estimation.

Literature also showed that there are differences between male and female
voices. The average female formant and fundamental frequencies are higher than
that of the male speaking voice [10, 11]. Therefore, the extraction of height
and age information in the speech signal is also affected by the gender of the
speaker [28]. Most of the previous speaker profiling studies considered gender
classification and height/age estimation as a joint problem [26, 27]. The only
work to use gender information and get a slight improvement in speaker’s
height and age prediction is by [22, 29]. Nevertheless, these works adapted
ground truth gender information which is fed to their model just as a binary
value. In order to capture distinctive representations of male and female
speech, we use two experts, one for each gender. Mixture of Experts (MoE) [30]
is a supervised learning system in which separate networks are designed to
handle different subsets of the training samples. Recently, the MoE
architecture has been studied for various tasks in the speech domain such as
multi-accent speech recognition [31], code-switching speech recognition [32],
etc.

##  3 Method

Table 1: Comparison of the proposed wav2vec 2.0 bi-encoder model with the existing work. Method | Height RMSE | Height MAE | Age RMSE | Age MAE
---|---|---|---|---
| Male | Female | Male | Female | Male | Female | Male | Female
Singh et al. [2] | 6.7 | 6.1 | 5.0 | 5.0 | 7.8 | 8.9 | 5.5 | 6.5
Kalluri et al. [21] | 6.85 | 6.29 | - | - | 7.60 | 8.63 | - | -
Kwasny et al. [25] | - | - | - | - | 7.24 | 8.12 | 5.12 | 5.29
Williams et al. [33] | - | - | 5.37 | 5.49 | - | - | - | -
Mporas et al. [15] | 6.8 | 6.3 | 5.3 | 5.1 | - | - | - | -
Shangeth et al. (single-task model) [27] | 8.1 | 6.0 | 5.9 | 4.9 | 6.96 | 7.6 | 4.8 | 5.1
Shangeth et al. (multi-task model) [27] | 7.5 | 6.5 | 5.8 | 5.1 | 6.8 | 7.4 | 4.8 | 5.0
Manav et al. (single-task model) [29] | 6.92 | 6.24 | 5.20 | 4.95 | 7.20 | 7.10 | 5.04 | 5.02
Manav et al. (multi-task model) [29] | 6.95 | 6.44 | 5.26 | 5.15 | 7.81 | 8.60 | 5.50 | 5.89
wav2vec 2.0 bi-encoder (ours) | 7.3 | 6.43 | 5.58 | 5.07 | 5.54 | 6.49 | 3.96 | 4.48
Table 2: Comparison of wav2vec 2.0 bi-encoder model with wav2vec 2.0 single-encoder model. Method | Height RMSE | Height MAE | Age RMSE | Age MAE
---|---|---|---|---
| Male | Female | Male | Female | Male | Female | Male | Female
wav2vec 2.0 single-encoder | 7.17 | 6.39 | 5.35 | 5.08 | 5.71 | 6.98 | 4.05 | 4.90
wav2vec 2.0 bi-encoder | 7.3 | 6.43 | 5.58 | 5.07 | 5.54 | 6.49 | 3.96 | 4.48
Table 3: Comparison of different feature extractors: MFCC, filter bank, x-vectors and wav2vec 2.0. Method | Height RMSE | Height MAE | Age RMSE | Age MAE
---|---|---|---|---
| Male | Female | Male | Female | Male | Female | Male | Female
MFCC bi-encoder | 7.63 | 6.69 | 5.79 | 5.33 | 8.15 | 8.65 | 5.86 | 6.02
filter bank bi-encoder | 7.86 | 6.68 | 6.13 | 5.36 | 8.51 | 8.42 | 6.19 | 5.86
x-vectors bi-encoder | 8.02 | 6.79 | 6.11 | 5.46 | 7.66 | 8.89 | 5.5 | 5.82
wav2vec 2.0 bi-encoder | 7.3 | 6.43 | 5.58 | 5.07 | 5.54 | 6.49 | 3.96 | 4.48

###  3.1 Self supervised representation

Motivated by the success of self-supervised learning in the field of speech
recognition, we explore an SSL model, namely wav2vec 2.0 [8] in speaker
profiling. The wav2vec 2.0 model learns speech representations by solving
contrastive tasks in latent space. Specifically, wav2vec 2.0 is composed of 1D
convolution features extractor $f:X\rightarrow Z$ which expects raw audio
input $X$ and outputs latent representations $Z$, followed by transformer
encoders $e:Z\rightarrow C$, which provides contextual information. The
feature extractor outputs are quantised by a quantisation module.

###  3.2 Mixture of Experts

The Mixture of Experts (MoE) [30] concept is based on divide-and-conquer
principle. If the training dataset is known in advance to be divided naturally
into certain subspaces, then separated experts of these subspaces can be
trained. A gating network decides the weights to be assigned to each of the
expert views and then a weighted sum of all the expert views is taken. The MoE
architecture leads to less interference during backpropagation and can provide
faster training and better generalizability.

###  3.3 Model architecture

The model architecture has been described in Fig.1. First, wav2vec 2.0 is used
to extract features from the raw audio waveform. Then, utilising the MoE
concept, a bi-encoder transformer network is built on top of the extracted
features. For the two genders present in the TIMIT dataset, male and female,
we consider separate experts, $MaleExpert$ and $FemaleExpert$. These two
experts have identical architectures as illustrated in Fig. 1, having 6 layers
of self-attention based transformer encoder with 8 attention heads in each
layer [14]. We take an average and standard deviation of the output of the
transformer encoder along the frame dimension and concatenate them to get
utterance level representations. These utterance level representations are
then fed to dropout and fully connected layers to complete the expert
architecture. The two separate experts can focus on gender-based audio
characteristics important for estimating height and age, while the fine-tuned
wav2vec 2.0 can learn common-level features for both genders.

The two experts, $MaleExpert$ and $FemaleExpert$ provide two expert views
$e_{m}$ and $e_{f}$:

$e_{m}=MaleExpert(x)$ (1) $e_{f}=FemaleExpert(x)$ (2)

where $x$ is the feature representation extracted from fine tuned wav2vec 2.0.
$e_{f}$ and $e_{m}$ are concatenated and fed to a fully connected layer with
sigmoid activation to predict the gender $g\in[0,1]$. Male gender is encoded
as 0 and female as 1. Gender prediction acts as a gating network to combine
the output of the two experts as follows:

$e=(1-g)\times e_{m}+g\times e_{f}$ (3)

$e$ is then fed to fully connected layers to perform age and height
regression.

###  3.4 Loss function

In our multi-task model, three losses are factored into the training process,
namely height, age and gender. Prior approaches for building a multi-task
model for speaker profiling have used a weighted sum of these losses [27, 29],
where the loss weights are manually fine-tuned. Instead, we use uncertainty
loss [12], which uses homoscedastic uncertainty to combine multiple losses.
Using this, we define our multi-task loss function as:

$\mathcal{L}=\frac{\mathcal{L}_{height}}{2\sigma^{2}_{height}}+\frac{\mathcal{L}_{age}}{2\sigma^{2}_{age}}+\frac{\mathcal{L}_{gender}}{2\sigma^{2}_{gender}}+log(\sigma_{height}\sigma_{age}\sigma_{gender})$
(4)

where $\sigma_{height}$, $\sigma_{age}$ and $\sigma_{gender}$ are learnable
parameters. As suggested in their paper, we make the substitution
$s_{height}=log(\sigma^{2}_{height})$, $s_{age}=log(\sigma^{2}_{age})$ and
$s_{gender}=log(\sigma^{2}_{gender})$ in equation 4 for numerical stability.

###  3.5 Mixup

We use mixup as a regularization technique to encourage the model to learn
linear inference from the input audio. Mixup augmentation has been used
previously in text-independent speaker verification [34].

Given two audio samples $x_{i},x_{j}$, with $h_{i},h_{j}$, $a_{i},a_{j}$ and
$g_{i},g_{j}$ as the corresponding height, age, and gender values, the mixup
augmented sample is defined as:

$x_{mixup}=\lambda x_{i}+(1-\lambda)x_{j}$ (5) $h_{mixup}=\lambda
h_{i}+(1-\lambda)h_{j}$ (6) $a_{mixup}=\lambda a_{i}+(1-\lambda)a_{j}$ (7)
$g_{mixup}=\lambda g_{i}+(1-\lambda)g_{j}$ (8)

where $\lambda\sim U(0,1)$. To deal with audios of different lengths, we
repeat the audio of a shorter length to make it of the same length as the
longer audio.

Table 4: Percentage change in RMSE values due to phoneme masking Mask | Height RMSE | Age RMSE
---|---|---
| Male | Female | Male | Female
Vowels | 2.04% | 0.07% | 38.9% | 20.46%
Nasals | -0.52% | 0.08% | 2.51% | -0.27%
Semivowels | 0.45% | -0.32% | 12.2% | -0.68%
Affricates | 0.0% | 0.0% | 0.0% | 0.0%
Fricatives | 1.28% | 2.87% | 6.08% | -2.41%
Stops | 0.6% | 2.9% | 4.14% | 3.05%
Others | 1.07% | -2.9% | 5.84% | 12.17%

##  4 Experiments

###  4.1 Dataset

In this work, we use TIMIT dataset [35] for our experiments. It consists of
audio samples from 630 speakers, having eight different dialects of American
English. The height varies in the range of 145cm to 204cm, and age varies in
the range of 21 years to 76 years. TIMIT dataset comes pre-divided into train
and test sets and we randomly select 15% of train set for validation.

###  4.2 Experimental Setup

For the proposed bi-encoder transformer model, apart from wav2vec 2.0, we also
consider other features for comparison: MFCC, filter bank and x-vectors. For
filter bank, we consider 80 mel bins along with first and second order delta
features. For MFCC, we consider 16 cepstral coefficients, along with first and
second order delta features. For both filter bank and MFCC, we use apply
Cepstral Mean and Variance Normalization (CMVN) and use frame length of 25ms
and frame shift of 10ms. For x-vectors, we extract the frame-level features
before it’s statistical pooling layer, which is then fed to downstream bi-
encoder architecture. The x-vectors were pre-trained on VoxCeleb1 [36] and
VoxCeleb2 [37] training data.

In another experiment, to show the efficacy of bi-encoder architecture, we
compare the wav2vec 2.0 bi-encoder model with wav2vec 2.0 single-encoder
model. The wav2vec 2.0 single-encoder model has the same layers as the wav2vec
2.0 bi-encoder model, except for the fact that there’s only a single expert
for both genders and there’s no gating network. The output of the single
expert is fed to fully connected layers to perform age, height, and gender
estimation.

For models using wav2vec 2.0, we unfreeze the entire wav2vec 2.0 architecture
except for the first five convolution layers of the convolutional feature
extractor and use Adam optimizer [38] with learning rate $10^{-6}$. When using
MFCC, filter bank and x-vectors, we use Adam optimiser with learning rate
$10^{-5}$. Mixup was proposed as regularization technique for deep neural
network architectures to favour simple linear behaviour, and as such, cannot
be applied directly at audio-level for traditional feature extraction
techniques like MFCC and filter bank. As a result, we use mixup only when
utilising x-vectors and wav2vec 2.0 as common feature extractor.

###  4.3 Experimental Results

In Table 1, we compare the results of the wav2vec 2.0 bi-encoder model with
previous works. While many previous works built separate models for age and
height, or separate models for male and female, we compare our multi-task
model with all of them. We report our results both in root mean squared error
(RMSE) and mean absolute error (MAE). It can be observed that the model
achieves significant improvement in the age estimation task. Specifically, we
achieve RMSE error 5.54 years and 6.49 years, which corresponds to relative
improvement of 18.5% in male age estimation and 8.6% in female age estimation
over the current state-of-the-art.

The comparison of wav2vec 2.0 bi-encoder vs wav2vec 2.0 single encoder model
has been illustrated in Table 2. The wav2vec 2.0 bi-encoder model achieves a
relative improvement of 2.9% for male age estimation and 7.0% for female age
estimation over wav2vec 2.0 single-encoder model in terms of RMSE error,
testifying our hypothesis of using separate encoders for the two genders.
However, the same pattern is not observed in height estimation results,
indicating that the bi-encoder architecture is not as useful for height
estimation as it is for age estimation.

In Table 3 we tabulate the results of different feature extractors: MFCC,
filter bank, x-vectors and wav2vec 2.0. It can be observed that wav2vec 2.0
feature representation is superior to other feature extraction techniques.

###  4.4 Phonetic importance analysis

In order to understand the relative importance of the different types of
phones in speaker profiling, we analyse the results of wav2vec 2.0 bi-encoder
model after masking all utterances of a particular phone type. TIMIT dataset
provides time-aligned phonetic transcriptions for each audio sample. Phones
used in TIMIT corpus are divided into the following classes: ‘Stops’,
‘Affricates’, ‘Fricatives’, ‘Nasals’, ‘Semivowels and Glides’, ‘Vowels’ and
‘Others’. For each of these phone types, we mask all the phones in audio
samples in TIMIT’s test set, and then calculate the height and age RMSE
scores. The relative percentage change due to these phone masking, as compared
to when no masking was done, are tabulated in Table 4. We notice the largest
increase in age RMSE value due to ‘Vowel’ masking, implying vowel sounds are
the most important phoneme type for age estimation. Surprisingly, we notice no
significant change in height RMSE values, indicating that height estimation is
not dependent on any particular phoneme type.

##  5 Conclusions

In this work, we presented a Mixture of Experts (MoE) based bi-encoder
transformer model, that uses self-supervised representation, specifically
wav2vec 2.0, for speaker age and height estimation. The experimental results
demonstrate that having separate experts for male and female voices can reduce
interference during training process and can achieve state-of-the-art results
for age estimation. We employed the homoscedastic uncertainty principle to
combine multiple losses of our multi-task model. As part of future work, we
plan to explore different feature extractors and self-supervised learning to
further improve age and height estimation results.

##  6 Acknowledgements

The computational work for this article was partially performed on resources
of the National Supercomputing Centre, Singapore (https://www.nscc.sg).

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