# Who's speaking? Predicting speaker profession from speech

Yaru Wu, Lihu Chen, Benjamin Elie, Fabian M. Suchanek, Ioana Vasilescu, Lori Lamel

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Yaru Wu, Lihu Chen, Benjamin Elie, Fabian M. Suchanek, Ioana Vasilescu, et al. Who's speaking? Predicting speaker profession from speech. 20th International Congress of Phonetic Sciences (ICPhS2023), Aug 2023, Prague, Czech Republic. pp.3086-3090. hal-04190126

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# WHO'S SPEAKING? PREDICTING SPEAKER PROFESSION FROM SPEECH

Yaru Wu¹,³,⁴; Lihu Chen²,*, Benjamin Elie³,*, Fabian Suchanek², Ioana Vasilescu³, Lori Lamel³

¹CRISCO/EA4255, Université de Caen Normandie, 14000 Caen, France

²Télécom Paris, Institut Polytechnique de Paris, France

³LISN, Univ. Paris-Saclay, 91405 Orsay cedex, France

⁴Laboratoire de Phonétique et Phonologie (UMR7018, CNRS-Sorbonne Nouvelle), France

yaru.wu@unicaen.fr, lihu.chen@telecom-paris.fr, elie@lisn.fr, fabian.suchanek@telecom-paris.fr,
{ioana.vasilescu, lori.lamel}@lisn.fr

## ABSTRACT

Variations in speech can reveal the gender, birth place, age, and socio-economic level of the speaker. In this paper, we show that even the profession of the speaker can be recovered from a recording. For this purpose, we design a method that combines features from both the speech signal and the transcription. For the features from the transcription, we used pre-trained language models. This allows us to train a model that predicts the speaker profession from both signals. Our empirical results show that our model can narrow down the profession of the speakers considerably.

**Index Terms:** knowledge base, large corpora, multimodal representation, pre-trained language models

## 1. INTRODUCTION

The recording of someone's speech provides two principal types of signals: the content of the speech (*what* is being said), and the acoustic and prosodic features of the speech (*how* it is being said). The latter features cover both segmental and supra-segmental levels and are reflected in various measures related to the acoustic properties of vowels and consonants, as well as to the prosodic specificity of such units (e.g., segment duration, pitch, speech rate, etc.).

It is known that these features can help predict the background of the speakers, most notably the age, gender, and the region of birth [1, 2, 3]. In this work, we take this analysis one step further: we show that even the profession of the speaker can to some degree be determined from a recording alone. We can show that the content of the speech alone is not sufficient for this purpose. Nor are the speech-based features. However, if both features are combined, we can predict the profession of the speaker with an accuracy of up to 76%. It matters thus not only *what* the speaker says, but also *how* they say it.

Our approach takes as input a speech recording of a speaker and its transcription. It then extracts features from both the speech signal and the transcription, using pre-trained language models for the latter. We then use a machine learning model to predict the speaker profession from these features. For our experiments, we use a corpus of French oral broadcast news from radio and TV channels recorded in the 2000s. As ground truth for training and evaluation, we use profession annotations from the YAGO knowledge base [4] that have been found by previous work [5].

The ability to predict the profession of the speaker can have implications for AI applications such as speaker classification, speaker identification, dialog systems, intelligent assistants etc. It also has ramifications in the domain of privacy: it shows that machines can identify a socio-economic characteristic of speakers, possibly unbeknownst to them or against their will. It is thus important to investigate to what degree such privacy violations are possible with today's means.

## 2. RELATED WORK

Variation is ubiquitous in human language and is linked to both physiological and psychological properties of the individuals who communicate. According to [6], physiological properties are related to age, gender, health etc., whereas psychological ones are related to their identity as individuals and position or role within a community. Both properties translate into an array of features ranging from very basic (physical) properties of the acoustic speech signal, to high level features (the semantics of the utterances). On the speech processing side, the features cover both acoustic and prosodic levels, ranging from acoustic parameters at the phone level (for instance, the formants of vowels), to prosodic parameters such as duration, fundamental frequency and intensity [7, 8, 9, 10, 11]. Of these features, acoustic properties of consonants and vowels as well as fun-

<footnote>*These authors contributed equally to this work.</footnote>
damental frequency related measures appeared to be speaker specific and have allowed to quantify inter-speaker variability [7, 11].

On the textual side, Natural language processing (NLP) methods have been used for a variety of prediction tasks about the speaker from discourse, such as political profiling [12], ideology classification [13], candidate to job matchings [14], or stance detection [15]. Closer to our approach, recent approaches combine speech and NLP features for more accurate prediction of speaker characteristics. For instance, in [16] the speaker stances in Mandarin ideological debate competitions are predicted by combining n-grams and acoustic prosodic features. In a similar direction, another approach [17] combines text and acoustics for ideology detection in Youtube data. However, the prediction of a socioeconomic indicator as precise as the speaker profession has so far been out of reach.

Closest to our approach is the work of [5], whose dataset we use here. While that work found correlations between speech features and professions, it was unable to *predict* the profession from the speech features. This is because the speech features alone are not sufficient for that task, as our work shows. Only the combination with the speech content that we propose here allows a narrowing-down of the profession.

## 3. DATA AND METHODOLOGY

Given as input a recording of speech by a speaker with its transcription, and given a list of professions, our goal is to predict the profession of the speaker. Our approach proceeds in three steps: (1) feature extraction from the speech, (2) feature extraction from the transcription of the speech, and (3) classification of the feature vector to one of the professions. To train the model of step (3), and to evaluate our method, we need a ground truth, i.e., an annotation of speakers with professions for part of the data.

### 3.1. Feature extraction from speech

The first step of our method extracts features from speech signal. We focus on global features, computed at the speech chunk level. This level corresponds roughly to an utterance. This choice is made in order to avoid considering speaker-specific features that could be irrelevant for a speaker profession recognition. Computing global features at a larger time-scale also enables us to extract psychological variations of speech. The following features were extracted from the speech signal : speech rate, mean pitch, pitch span, pitch declination, normalized pitch peak extent, local peak dynamics of pitch, mean for

each of the 4 formants, formant span for each of the 4 formants, voicing ratio and devoicing ratio. Speech rate is calculated on speech utterances between pauses that are equal to or greater than 200ms. The pitch and formant span are defined as the difference between the average distance between the maximums and the local mean contour and the average distance between the minimums and the local mean contour [18]. We extracted and computed the characteristics of the 4 first formant trajectories – hence the 4 mean formants and 4 formant spans in the table. Voiced and unvoiced ratio relate to non-canonical surface forms, corresponding to stops that changed their voicing category. Voicing refers to canonical /ptk/ pronounced [bdg], while unvoicing refers to canonical /bdg/ pronounced [ptk]. The estimation of non-canonical realizations is obtained with the method described in [19]. We then compute the ratio of produced non-canonical forms in each category (voicing and devoicing) by the speaker as marks for consonant reduction (voicing) or strengthening (devoicing) that may be speaker specific. Except for the voiced and unvoiced ratio, which are computed over all speech chunks of a particular speaker, features are computed at the speech chunk level, and then averaged over each speaker. Each speaker is then assigned the mean value and the standard deviation of each features. This results in a feature vector **s** of length *N* = 30 for a given speech recording.

### 3.2. Feature extraction from the transcription

Formally, the transcription of a speech recording is a word sequence of length *l*: $W = \{w_1, w_2, ..., w_l\}$. We extract a textual feature vector **t** ∈ R^n from the sequence by using a encoder function $\phi(\cdot)$, **t** = $\phi(W)$. The resulting vector **t** can be regarded as a document-level representation for the input transcription text. We adopt Pre-trained Language Models (PLMs) [20] as an encoder to extract textual features from transcriptions for speakers because PLMs have achieved the state-of-the-art across a series of Natural Language Processing tasks. More specifically, we use CamenBERT [21, 22], which targets French texts. The input transcription is truncated into word sequences with a maximum length of 500, and CamenBERT is applied to the input. Afterward, a pooling layer is used to the whole sequence of the last-layer hidden states to obtain the textual feature **t**. We use a CamenBERT-large model¹ of dimension *n* = 1048. When a transcription is missing, we use a zero padding vector.
<table><caption>**Table 1:** Statistics of the top-8 frequent professions.</caption><thead><tr><th>Profession</th><th>Politician</th><th>Journalist</th><th>Artist</th><th>Movie Person</th><th>Scientist</th><th>Lawyer</th><th>Business Person</th><th>Sports Person</th><th>All</th></tr></thead><tbody><tr><th>Number of speakers</th><td>189</td><td>56</td><td>53</td><td>70</td><td>23</td><td>12</td><td>24</td><td>21</td><td>448</td></tr><tr><th>Utterances per speaker</th><td>505</td><td>1869</td><td>881</td><td>519</td><td>600</td><td>506</td><td>241</td><td>132</td><td>695</td></tr></tbody></table>

<table><caption>**Table 2:** Top-k keywords extraction from all transcripts using YAKE [23]: Top-5 keywords for professions with sufficiently large samples; top-3 for those with fewer samples.</caption><thead><tr><th>Profession</th><th>Keywords</th></tr></thead><tbody><tr><td>Politician</td><td>France, politique, gouvernement, ministre, pays</td></tr><tr><td>Journalist</td><td>France, heures, président, Inter, soir</td></tr><tr><td>Artist</td><td>temps, film, films, cinéma, France</td></tr><tr><td>Movie Person</td><td>France, films, français, personnage, film</td></tr><tr><td>Scientist</td><td>France, monde, temps, Irak, Israel</td></tr><tr><td>Lawyer</td><td>vérité, société, France</td></tr><tr><td>Bussiness Person</td><td>travailler, monde, année</td></tr><tr><td>Sports Person</td><td>France, l'équipe, défense</td></tr></tbody></table>

<table><caption>**Table 3:** Performances (F1 score) of profession prediction with speech signal and textual features. Macro F1 and Accuracy for **Both** column. Column “M” gives the rate of speakers without transcriptions.</caption><thead><tr><th rowspan="2">Profession</th><th colspan="2">Speech signal</th><th colspan="2">Text</th><th colspan="2">Both</th></tr><tr><th>F1</th><th>M</th><th>F1</th><th>M</th><th>F1</th><th>Acc</th></tr></thead><tbody><tr><td>Politician</td><td>70.9</td><td>65.9</td><td>41%</td><td><strong>77.2</strong></td><td>76.6</td><td></td></tr><tr><td>Journalist</td><td>22.6</td><td>38.4</td><td>29%</td><td><strong>44.0</strong></td><td>42.3</td><td></td></tr><tr><td>Artist</td><td>15.6</td><td>16.3</td><td>30%</td><td><strong>19.1</strong></td><td>13.6</td><td></td></tr><tr><td>Movie Person</td><td>49.6</td><td>8.1</td><td>74%</td><td><strong>60.1</strong></td><td>60.6</td><td></td></tr><tr><td>Scientist</td><td>6.7</td><td><strong>12.5</strong></td><td>26%</td><td>6.1</td><td>5.7</td><td></td></tr><tr><td>Lawyer</td><td>0.0</td><td>0.0</td><td>33%</td><td><strong>12.5</strong></td><td>15.4</td><td></td></tr><tr><td>Business Person</td><td>0.0</td><td>18.2</td><td>33%</td><td><strong>27.8</strong></td><td>22.9</td><td></td></tr><tr><td>Sports Person</td><td>14.8</td><td><strong>38.9</strong></td><td>14%</td><td>35.3</td><td>34.3</td><td></td></tr><tr><td>All</td><td>22.6</td><td>24.5</td><td>41%</td><td><strong>35.3</strong></td><td>33.9</td><td></td></tr></tbody></table>

<table><caption>**Table 4:** Hit@K performance with combined speech and transcription features.</caption><thead><tr><th></th><th>Hit@1</th><th>Hit@2</th><th>Hit@3</th></tr></thead><tbody><tr><td>Politician</td><td>66.3</td><td>92.9</td><td>96.3</td></tr><tr><td>Journalist</td><td>48.6</td><td>67.7</td><td>77.1</td></tr><tr><td>Artist</td><td>19.0</td><td>26.6</td><td>38.4</td></tr><tr><td>Movie Person</td><td>54.1</td><td>65.5</td><td>72.5</td></tr><tr><td>Scientist</td><td>22.2</td><td>30.1</td><td>30.3</td></tr><tr><td>Lawyer</td><td>0.0</td><td>10.2</td><td>20.1</td></tr><tr><td>Business Person</td><td>30.3</td><td>32.3</td><td>33.4</td></tr><tr><td>Sports Person</td><td>50.0</td><td>68.2</td><td>68.2</td></tr><tr><td>All</td><td>55.8</td><td>68.5</td><td>74.3</td></tr></tbody></table>

### 3.3. Model prediction

We now have, for the given speech recording, a feature vector $s \in \mathbb{R}^m$ of speech features, and a feature vector $t \in \mathbb{R}^n$ of text features. We combine both vectors by concatenation, and obtain a multimodal representation $u \in \mathbb{R}^{m+n}$.

We then classify this vector using a Multi-Layer Perceptron (MLP) with one hidden layer [24]. We have tested different architectures and sets of input

features by Leave-one-out cross-validation across speakers. The best results were obtained with a hidden layer size of 100, a RELU activation function, and a L2 penalty parameter of 1 for regularization. The number of finally selected features is reduced to 26, as some speech features are non-discriminant for this task. The rejected features are the standard deviation of the 3rd formant span, the mean value of the averaged 3rd formant, and the mean values of the averaged pitch and the pitch span. This can be explained by the fact that averaged values of the fundamental frequency and higher formants are expected to be more dependent on speaker characteristics (e.g. gender) than on speaker profession.

## 4. EXPERIMENTS

### 4.1. Dataset

We use the annotated dataset from [5]. It consists of the ESTER corpus [25] (80 hours of French formal journalistic speech, mainly from radio broadcast news) and the ETAPE corpus [26] (about 40 hours of less formal journalistic French speech data, mainly from debates and interviews). The two corpora were transcribed manually, and 66% of the utterances have an associated manual transcription. The transcription was force-aligned with the speech signal using the LISN (former LIMSI) speech transcription system [27, 28]. The speech transcription system was used to segment the speech data automatically to word and phone levels. Pauses, hesitations or breaths were detected automatically by the system. The minimum segment duration is 30ms, corresponding to 3 frames [29]. The dataset contains only utterances whose speaker could be identified. We combine all utterances per speaker, so that we arrive at one (pseudo-) recording per speaker. The dataset contains 342 distinct named speakers. For each speaker, the dataset also contains the ground truth in the form of a profession from the YAGO knowledge base [4]. Table 1 shows the statistics of the professions in our dataset.

### 4.2. Using only the speech signal

The performance results for profession prediction by the speech signals alone are shown in Table 3, in the column “Speech Signal”. The best scores are obtained for Politician (F1=70.9) and Movie Person (F1=49.6). Both professions and in particu-
lar *Politician* are well represented in the two cor-
pora. *Politicians* exhibit idiosyncratic patterns such
as slow speech rate and high rate of disfluencies.
They also have a tendency to consonant reduction
through increased rate of non-canonical voiced con-
sonants. This is in line with previous findings [5].
Beyond that, the median value of the speech rate
across *Politicians* is the lowest among the profes-
sions. These trends can be associated with the pro-
fession. Reduction can be linked to a rather spon-
taneous and thus relaxed pronunciation and one can
speculate that *Politicians* are used to speaking pub-
licly and their speech is rather relaxed. *Movie Per-
sons*, too, can be assimilated to trained public speak-
ers and exhibit recognizable speaking traits resulting
in a more spontaneous, relaxed speech. The scores
for the other professions are much lower, indicating
that speech signals alone cannot predict the profes-
sion.

4.3. Using only the speech transcriptions

The performance results for profession prediction
using only the transcriptions are shown in the third
column (“Transcription”) of Table 3. We first ob-
serve that the textual features are more helpful than
the speech features. The macro F1 has a lead of
1.9 absolute percentage points. To better under-
stand what triggers this performance, we extracted
the top keywords of each profession (Table 2). The
results are consistent with the trends we saw for
speech features: Politician and Movie Persons have
rather profession-specific and frequent keywords,
which explains the good performance of the textual
model. For instance, Politicians, often refer to “poli-
tique (politics)”, “gouvernement (government)” and
“ministre (minister)” in their speeches. We also no-
tice that the performances for the Movie Person and
Lawyer are extremely low. The former is caused by
the high missing rate of transcriptions (74%) and the
latter is a consequence of the small number of data
samples (12 samples). Artists as broader category
are also hard to distinguish from Movie Persons, as
they use overlapping topical keywords (e.g., “film”,
“cinema”).

4.4. Combining speech signal and transcription

We now combine the features from the speech sig-
nal and the text. The results are shown in the last
two columns of the Table 3. The combination con-
sistently performs the best, and the macro F1 across
all professions is at least 10 percentage points higher
than the other two (shown in the last row). This
proves the interest of combining low and high level
linguistic features: the integration of features ex-

tracted from both the speech signal and the text
can better characterize the speaker and consequently
narrow its profession. The result also suggests the
reasons why human strategies are more effective:
the human perception relies on a complex integra-
tion of both low and high level information and take
advantage of the communicative context in a broad
sense to decode speaker turns properties including
the characteristics of the speaker that utter it.

The accuracy of the model is not perfect. We hypothesize that the small number of training samples constrains the performance of machine learning. Furthermore, some parts of the audio did not have manual transcripts (column M). Nevertheless, our model can correctly predict the professions for more than half of the speakers. Table 4 shows the Hit@k measure, i.e., the percentage of people for whom the correct profession is among the top k predictions. We see that, while the approach cannot outright guess the profession, the speech nevertheless gives away enough information to narrow down the set of professions to 2 or 3.

5. CONCLUSION

We have shown that it is possible to narrow down,
or even predict, the profession of a speaker from
a recording of their speech. This is an interesting
finding that can be further deepened, e.g., by using
more features from speech, using more training data,
or using extrinsic features. The main finding under-
lines the complementarity of low and high level lan-
guage features and the need to combine them in or-
der to improve performance.

The results point to interesting avenues of re-
search, where intrinsic characteristics (e.g. age,
gender), meta-cognitive characteristics (e.g. cur-
rent state of mind or subconscious attitude towards
a topic), or socio-economic characteristics (e.g. so-
cial status) can be predicted from multi-level acous-
tic and linguistic features. Such work poses obvi-
ous challenges for the privacy of individuals. It is
thus important to investigate to which degree such
privacy violations are possible, and whether they re-
quire regulation. However, such work also brings
opportunities: It could help dialog systems adapt
their discourse to the user. For example, the expla-
nation of an insurance case would be different for a
lawyer and for a scientist. The work could also help
identify fake recordings or fake videos of public per-
sonalities, if the extracted characteristics do not cor-
respond to the known characteristics of the speaker.
Finally, such analyses carry educational and scien-
tific value, as they reveal speech characteristics that
give away personal details.
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