<figure><img src="image_1.png" /></figure>

HAL
open science

# Robust Vocal Intensity Prediction: Overcoming Dataset Bias with Pretrained Deep Models

Quentin Le Tellier, Marc Evrard, Albert Rilliard, Jean-Sylvain Liénard

► To cite this version:

Quentin Le Tellier, Marc Evrard, Albert Rilliard, Jean-Sylvain Liénard. Robust Vocal Intensity Prediction: Overcoming Dataset Bias with Pretrained Deep Models. Interspeech 2025, Odette Scharenborg; Catharine Oertel; Khiet Truong, Aug 2025, Rotterdam, Netherlands. pp.1728-1732, 10.21437/Interspeech.2025-2311. hal-05359416

HAL Id: hal-05359416

https://cnrs.hal.science/hal-05359416v1

Submitted on 11 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Robust Vocal Intensity Prediction: Overcoming Dataset Bias with Pretrained Deep Models

Quentin Le Tellier, Marc Evrard, Albert Rilliard, Jean-Sylvain Liénard

Université Paris-Saclay, CNRS, LISN, Orsay, France

quentin.le-tellier@lisn.fr, marc.evrard@lisn.fr, albert.rilliard@lisn.fr, jean-sylvain.lienard@lisn.fr

## Abstract

Vocal intensity prediction has been investigated in previous studies, where machine-learning (ML) models (e.g., linear regression, SVM) trained on basic speech features (such as spectrograms and Mel-spectrograms) have demonstrated good prediction accuracy at the utterance level. In this work, we revisit these methods by evaluating them on two calibrated datasets, including a bilingual corpus containing both French and English speech. Our findings show that prior approaches struggle to generalize across datasets. To address this limitation, we leverage embeddings from the Wav2Vec 2.0 model as input features to classical ML regressors. While previous work used such embeddings for vocal intensity classification, our study demonstrates their effectiveness in addressing cross-dataset generalization issues for the regression task. The results confirm that pretrained embeddings significantly improve generalization and mitigate overfitting issues linked to traditional acoustic features.

**Index Terms:** Vocal intensity prediction, cross-corpus evaluation, overfitting, Wav2Vec2, automatic speech analysis

## 1. Introduction

The quality of the vocal output varies in large proportions with changes in the speaker's vocal intensity. Speakers constantly adjust their vocal intensity to accommodate contextual changes. They increase loudness in noisy environments (Lombard speech) [1, 2] and adapt to the interlocutor's distance [3]. Clear or hyperarticulated speech is used to adapt to the listener's needs [4]. In microphone-mediated communication, lower-intensity speech, including whispered or creaky voice, tends to occur more frequently [5]. Unfortunately, the original level of a voice is generally lost due to uncontrolled recording conditions, with a varying distance to the microphone and unknown gain [6]. This original voice level, or voice strength, is defined as the C-weighted sound pressure level measured in free field at one meter in front of the speaker's mouth [7]. It is thus lost in most recordings, whether in the media, during videoconferencing, or in speech corpora available to the community.

Vocal intensity is a main aspect of the voice source, together with vocal folds tension, vibration modes, and noises [8]. It impacts the source's characteristics, typically its fundamental frequency and spectral slope [9]. Behavioral changes related to producing different intensity levels impact supraglottal articulation (e.g., rhythm, effort levels [2, 10]). Changes in the acoustic output linked with vocal intensity impact speech processing frameworks [11, 12], and are an important marker of pragmatic changes, as they relate to the primary dimension of affective changes in voice [13, 10]. The differences in vocal intensity are seldom considered in descriptions of speech characteristics or by speech processing tools, notably because this information is

not directly accessible due to recording practices. Nonetheless, the original voice strength of a vocal performance can be perceptually recovered from the amplitude-normalized signal [14]. It shows our cognitive capacity to use cues to infer the loudness of the voice and the pragmatic features it carries [15, 16]. A growing literature is published around the questions related to vocal effort and intensity (e.g., [17, 18]). It is partly due to its importance in speech therapy, as using loud voices in professional settings frequently leads to vocal strain [19]. However, few resources that propose calibrated audio recordings [6] are available to the community. In this landscape, a welcome effort was proposed by Alku et al. [20], who issued a corpus of calibrated recordings based on sentences read by several speakers with four vocal intensity categories (the AVID corpus). The authors also trained models to estimate the original intensity level of the recorded sentences using the spectral characteristics of the signal.

This paper presents a follow-up experiment aimed at reproducing the models proposed by [20] as a topline reference, while extending the analysis by exploring additional acoustic features and regression models. Crucially, we evaluate the generalization ability of these models for vocal intensity estimation on two external corpora. One includes affective variations [21], where changes in vocal intensity occur alongside other voice quality modifications associated with emotional expression [22]. The second corpus was recorded specifically for this study using a setup comparable to AVID, but under different conditions.

A key focus of our work is to assess whether models trained on AVID capture voice source characteristics in a way that generalizes to different contexts, or whether they rely on dataset-specific acoustic cues. These voice source characteristics include additive noise (breathiness), structural noise (creaky or harsh voice), and changes in vocal fold tension [23, 24], as well as supraglottal adjustments such as smiling or anteriorized articulation [10].

Our experiments revealed that models based on handcrafted features (e.g., spectrograms, Mel-spectrograms) and simple machine learning regressors exhibit strong performance on the training corpus but generalize poorly to other datasets with different recording conditions and voice quality variations. To address this issue, we investigated the use of pretrained deep speech representations, specifically Wav2Vec 2.0 embeddings, as input features. Although such embeddings have already been used for vocal intensity classification into discrete categories [25], our findings show that they also provide a practical and effective solution to the generalization problem in the regression setting. Combined with classical ML regressors, these representations significantly improve cross-dataset performance and help mitigate overfitting to training conditions.
Section 2 describes the corpora and the regression models trained on AVID and tested on the two additional datasets. The models' performances are described in Section 3 and discussed in Section 4. Finally, the conclusion addresses the implications of our findings for reliably estimating vocal intensity beyond controlled recording conditions.

# 2. Methods

## 2.1. Datasets

The AVID corpus includes speech produced by 50 speakers (25 male and 25 female speakers aged between 20 and 38) who recorded 25 English sentences with four categories of vocal intensity (only the AVID sentence reading task is used here). The speakers were recorded using a headset microphone positioned 5 cm from their mouth. The vocal intensity categories are described as “soft, normal, loud, and very loud,” and were presented to the speakers with a short description. The AVID-SENT subset, corresponding to the sentence reading task of the AVID corpus, contains 10,000 utterances evenly distributed across the four intensity categories. The authors’ analysis shows a significant overlap across these categories, with different speakers’ performances showing a large variability [20].

To test the models trained on AVID, the VIC dataset was recorded, containing a total of 800 read sentences with varying intensity. It was produced by seven speakers (four female and three male speakers aged between 23 and 62) reading 25 sentences with four categories of vocal intensity. Two speakers read the English sentences used in AVID, and six speakers read 25 equivalent sentences in French (one male speaker read the sentences in the two languages). Recording two languages was intended to test for a potential language effect. We adopted the recording procedure employed in AVID, incorporating four distinct intensity categories and mostly replicating the original setup. Recordings were conducted in a soundproof booth. Two omnidirectional microphones were used: a high-quality condenser headset microphone (DPA 6066-OC-R-F00, referred to as HSM) and a condenser pencil microphone (DPA 2006 A, 1/2 inch, referred to as PM). The PM was calibrated using a Brüel & Kjær 4230 sound level calibrator and placed 30 cm from the speaker, sitting on a fixed chair. The HSM was fitted on the head of the speaker (5 to 9 cm away from the center of the mouth). The microphone was calibrated by computing the average intensity ratio relative to the HSM across the 25 sentences recorded in the first condition.

A third corpus of acted emotions in French was used (Affective Dataset: AD). It was recorded for training an expressive Text-To-Speech synthesizer [21], using a female speaker trained in acting and singing performances. The corpus consists of seven affective subsets, each containing 160 identical sentences read in different affective voices: anger, fear, surprise, joy, sadness, sensuality, and a neutral expression. The speaker was recorded using a microphone located 40 cm from her mouth. An essential distinguishing characteristic among the seven affective expressions is the variation in vocal intensity [10]. These intensity differences correspond to variations in arousal across affective categories and are associated with differences in vocal effort from an articulatory perspective. The relationship between vocal effort and arousal has been described in the literature [22, 13]. In the present corpus, this relationship is manifested through substantial intensity variations (spanning approximately 35 dB), which are closely associated with marked

offsets in fundamental frequency, among other acoustic features [10].

## 2.2. Experimental setup

The first step involved reproducing the models proposed by [20]. The second step extended the analysis by exploring alternative modeling approaches using different speech signal representations. The speech signals were resampled at 16 kHz, followed by peak normalization to standardize amplitude, ensuring that the highest peak in each signal reached a predefined target level. This normalization process was designed to remove direct signal intensity information, ensuring that the extracted features captured intrinsic speech properties rather than mere amplitude variations. Feature extraction was subsequently performed on these normalized signals.

### 2.2.1. Input features

Spectrograms and Mel-spectrograms (Mel-spec) were extracted using the Librosa library on 25 ms length Hann window with a 5 ms overlap (to replicate [20]). A 1024-point Fast Fourier Transform (FFT) was performed. The Mel-spec comprised 128 filters with no constraints on minimum and maximum frequencies, resulting in a maximum frequency equal to the Nyquist frequency. Each feature's mean and standard deviation were extracted along the time axis, resulting in 1026-dimensional (1026-D) spectrogram features and 256-D Mel-spec features.

A self-supervised model, wav2vec 2.0 [26] (here referred to as wav2vec) was also used as a feature extractor<fnref n="1" /> to leverage its robust representations of speech. Hidden representations from each of its 24 layers were extracted and averaged along the time axis, resulting in 1024-dimensional (1024-D) feature vectors per layer. To identify which layer of Wav2Vec provides the most effective representations for vocal intensity prediction, we extracted embeddings from each layer and trained a separate ML model for each. These models were then evaluated on the AVID corpus using cross-validation to compare their performance across layers.

### 2.2.2. Hyper-parameters and model selection

A comprehensive grid search was performed for a total of 3,420 combinations of features, models, and hyperparameters. The models explored in the grid search include linear regression, support vector machine regressor (SVR), k-nearest neighbors (KNN), random forest (RF), and gradient boosting (GB). Only a curated list of the extracted features, models, and hyperparameters explored in the grid search is presented here for clarity. We selected the ones exhibiting the most significant results: linear regression and SVR. For the SVR, the explored hyperparameters were: C = [0.01, 0.1, 1, 10, 100], gamma: "auto".

### 2.2.3. Evaluation metrics and confidence intervals

Several metrics were selected to evaluate the performance of the vocal intensity regression model: the root mean squared error (RMSE), the coefficient of determination ($R^2$), and the square of the Pearson correlation coefficient ($r^2$).

To assess the statistical significance of the reported metrics, 95% confidence intervals were computed using 1000 bootstrap samples. To account for intra-speaker dependencies, we applied

<footnote n="1">¹The facebook/wav2vec2-large-960h-lv60-self model was selected.</footnote>
speaker-level bootstrapping. When a speaker was sampled, all of their utterances were included in the sample, preserving the dependency structure within speakers. Training and validation splits were thus speaker-independent. All computations were performed using the Confidence Intervals library [27].

### 2.2.4. *Intensity labels*

Following [6], we computed the Z-Frequency, F-Time weighted equivalent continuous sound level ($L_{eqZF}$) for each sequence of the three datasets. The values were computed with the same mouth-to-microphone distance for all datasets, to account for the influence of microphone distance on sound pressure level measurements.

### 2.2.5. *Experiments*

All models were trained on the training portion of the SENT subset from the AVID dataset, and all hyperparameters were selected using 5-fold cross-validation. The dataset was split into 80% for training and 20% for testing. In addition to enforcing speaker independence, as outlined in Section 2.2.3, the split was stratified by speaker gender to ensure a balanced distribution across subsets. The training subset thus includes 40 speakers (20 males and 20 females), and the test set consists of 10 speakers (5 males and 5 females).

These best-performing models were then evaluated on the AD and VIC datasets to estimate their prediction performance and assess their generalization capability. This evaluation provided insights into how well these ML pipelines can adapt to datasets with different recording conditions and speech content (change in language and style).

## 3. Results

Table 1 presents the $R^2$ and $r^2$ values and their confidence intervals for different feature sets and models on the AVID validation subset, as well as VIC and AD test sets.

### 3.1. Performance by dataset

On the AVID validation subset, the best results for both $R^2$ and $r^2$ are obtained with the wav2vec feature set. One can note that both metrics exhibit identical results on this dataset. The spectrogram features (STFT) set presents competitive results, with the highest values with the SVM model achieving 0.91 ± 0.01, which confirms that we could closely reproduce [20]'s topline results, linear regression exhibiting overlapping results considering their confidence intervals.

On the VIC and AD datasets, the performance of STFT-based models dropped considerably, with more pronounced degradation observed in linear regression models compared to SVMs. In contrast, Mel-spec-based models, although underperforming on the AVID dataset, exhibited a comparatively smaller decrease in performance. Wav2vec-based models yield the best results both in terms of $R^2$ and $r^2$, and comparable performance to the best models on AVID in terms of $r^2$.

### 3.2. Performance comparison across datasets

Due to approximations in the average mouth-to-microphone distance within the AD dataset, the results exhibit a systematic offset between the model predictions and the actual values computed on the dataset. Nevertheless, strong linear correlations between predicted and actual intensity values are observed, as indicated by the $r^2$ scores computed across both datasets, particu-

larly for the best-performing models. These models further enabled the estimation of the mean intensity offset attributable to variations in mouth-to-microphone distances between the two datasets. This offset does not pose a substantial issue in the context of our work on vocal intensity, as the primary objective is to model relative variations in intensity rather than to recover absolute SPL values. The difference between the two datasets was estimated by calculating the average residuals of the best-performing models on the AD dataset.

To fairly compare the performance of the models across the three datasets, we thus focused on the $r^2$ scores, indicating the linear correlation between the predictions and the measured values. A comparison of the $r^2$ scores across the three datasets (see Table 1) reveals that while some of the models achieving the highest performance on the AVID dataset also generalize well to the AD dataset, others exhibit weak generalization capabilities. Typically, the models based on spectrogram features, which show the highest performances on AVID, showed lower generalization capabilities on the VIC and AD datasets (cf. Table 1). Comparatively, the Mel-spec-based models, which showed lower $R^2$ and $r^2$ performances on AVID, have better generalization performances on the VIC and AD datasets. The best generalization is obtained using the wav2vec extracted features, which showed comparable performances (within the bootstrap-based confidence intervals) both on AVID and on the VIC dataset (Table 1).

<figure><img src="image_3.png" /><figcaption>Figure 1: True vs. predicted SPL plots for the first layer of wav2vec features on the Affective dataset</figcaption></figure>

### 3.3. Vocal intensity estimation in affective speech

Figure 1 presents a scatter plot of actual vs. predicted intensity values obtained by the linear model trained on features extracted from the first layer of wav2vec, with predictions performed on the AD dataset. It allows comparing the spectrogram-based with the wav2vec-based models for their generalization capabilities. Both models were selected based on their highest $R^2$ scores on the AVID dataset, in order to provide a reliable estimate of the generalization performance associated with each feature representation.

We can observe that the $r^2$ score between actual and predicted values of the wav2vec-based model is higher than for the spectrogram-based models, with respective scores of 0.88 ±
<table><caption>Table 1: Mean and confidence intervals of the measures obtained for different algorithms (linear regression and SVR) with various feature sets across the three datasets. The hyperparameters of the selected models were tuned on AVID.</caption><thead><tr><th>Feature</th><th>Model</th><th>ΑΙΔ R<sup>2</sup></th><th>ΑΙΔ r<sup>2</sup></th><th>VIC R<sup>2</sup></th><th>VIC r<sup>2</sup></th><th>AD r<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="2">STFT</td><td>linear regression</td><td>0.89 ± 0.02</td><td>0.89 ± 0.02</td><td>-0.23 ± 0.25</td><td>0.21 ± 0.07</td><td>0.17 ± 0.04</td></tr><tr><td>SVR</td><td>0.91 ± 0.01</td><td>0.91 ± 0.01</td><td>0.47 ± 0.19</td><td>0.47 ± 0.23</td><td>0.41 ± 0.04</td></tr><tr><td rowspan="2">Mel-spec</td><td>linear regression</td><td>0.79 ± 0.03</td><td>0.79 ± 0.03</td><td>0.72 ± 0.11</td><td>0.73 ± 0.13</td><td>0.75 ± 0.02</td></tr><tr><td>SVR</td><td>0.82 ± 0.03</td><td>0.82 ± 0.03</td><td>0.71 ± 0.11</td><td>0.71 ± 0.13</td><td>0.78 ± 0.02</td></tr><tr><td rowspan="2">wav2vec</td><td>linear regression</td><td>0.92 ± 0.01</td><td>0.92 ± 0.01</td><td>0.83 ± 0.04</td><td>0.90 ± 0.05</td><td>0.82 ± 0.02</td></tr><tr><td>SVR</td><td>0.91 ± 0.01</td><td>0.91 ± 0.02</td><td>0.80 ± 0.07</td><td>0.88 ± 0.07</td><td>0.87 ± 0.01</td></tr></tbody></table>

0.01 and 0.36 ± 0.02. These two models received comparable R² and r² values on AVID (cf. Table 1).

After correction for the mean error, the selected wav2vec-based model achieved an RMSE of 2.92 ± 0.12 dB on the AD dataset. This performance is comparable to that of the best-performing spectrogram-based models evaluated on the AVID dataset, which yielded an RMSE of 2.29 ± 0.25 dB.

## 4. Discussion

The proposed alternative models, based on features extracted using wav2vec 2.0, achieved performance comparable to traditional methods relying on spectrogram features on the AVID corpus. These results suggest that both model types effectively capture the acoustic characteristics of vocal intensity, yielding high R² values (0.92) and enabling a reliable estimation of the original vocal intensity on this dataset. Among the best models on the AVID dataset, spectrogram-based models did not generalize well on the AD and VIC datasets. In contrast, the wav2vec-based models exhibited better generalization capabilities on the AD dataset, with similar values of r² on the two evaluation corpora. These Transformer-based models also generalized well on the VIC dataset, especially along with the SVR algorithm. In contrast, models based on other feature sets struggled to estimate vocal intensity from expressive voices. We also showed that using Transformer-based models makes it possible to estimate the original vocal intensity independently of the recording settings (distance to the microphone and gain) with satisfactory precision at the sentence level.

The difference in generalization capabilities is striking between models based on traditional acoustic features, trained solely on the AVID corpus, and a model based on Transformer features pretrained on large-scale speech datasets. This phenomenon is likely because these large datasets include complex voice quality variations, whereas AVID lacks expressive changes beyond vocal intensity. The Transformer-based models are globally robust to affective variations (as shown by Figure 1), while spectrogram-based models struggle in this task. Notably, an interesting result of this paper comes from the Mel-spec-based models, also trained on AVID only, that have thus not seen expressive changes. Although they exhibit lower R² performance on the AVID corpus, they show better generalization capabilities than the spectrogram-based models (cf. Table 1 for r² on the AVID and AD datasets).

There were no observable effects of the language (English or French) or the microphone (HSM or PM) on the performance measured on the VIC dataset. Therefore, the performance drop appears to be caused by overfitting to aspects specific to the AVID corpus by the model trained on spectrogram features. In contrast, cruder descriptors such as the Mel-spectrograms al-

ready provided a reasonable approximation of the acoustic characteristics of vocal intensity across different speakers.

The Transformer features already embed complex representations of voice quality, intertwining the vocal intensity dimension with other characteristics such as pitch register, vocal fold tension [8, 28], and supraglottal settings like smiling [10]. This richer encoding enables better generalization. On the contrary, fear samples tend to be overestimated, similarly to expressions at the lower end of the vocal intensity spectrum, such as sadness and sensuality. These results suggest that estimating vocal intensity in expressive speech could be improved.

## 5. Conclusion

The predictions of vocal intensity from the AVID corpus were reproduced with performance comparable to that reported in the original publication [20]. We also proposed other models that exhibit comparable performances on the AVID corpus, based on features extracted using wav2vec. While the Transformer-based models do not outperform the topline reference, they demonstrate better generalization capabilities to other datasets, especially for the AD dataset that contains expressive vocal changes. These improved generalization capabilities also extend to models trained on Mel-spectrograms. Although their performance is lower on AVID, they achieve better results on the other datasets. The best performance on all datasets is achieved with the wav2vec-based models. When developing models for vocal intensity prediction, training on datasets produced under varied recording conditions may be beneficial to reduce overfitting and improve generalizability.

It is noteworthy that expressive voice qualities, such as breathy and tense voices in this study, appear to influence the estimation of vocal intensity. Consequently, training on datasets encompassing diverse voice production styles may be considered to improve the estimation of vocal intensity from uncalibrated corpora featuring varied speaking styles and linguistic backgrounds.

Future work could aim to disentangle the different dimensions of voice source features, such as vocal effort, vocal fold tension, and glottal noise components [23, 28, 8], in order to facilitate more accurate extraction of meaningful acoustic features from uncalibrated speech recordings.

## 6. Acknowledgements

This work was partly funded by the French ANR VERS project (ANR-23-CE38-0010-01).
7. References

[1] J.-S. Liénard and M.-G. Di Benedetto, "Effect of vocal effort on spectral properties of vowels," *The Journal of the Acoustical Society of America*, vol. 106, no. 1, p. 411–422, Jul. 1999.

[2] M. Garnier and N. Henrich, "Speaking in noise: How does the lombard effect improve acoustic contrasts between speech and ambient noise?" *Computer Speech & Language*, vol. 28, no. 2, p. 580–597, Mar. 2014.

[3] H. Traunmüller and A. Eriksson, "Acoustic effects of variation in vocal effort by men, women, and children," *The Journal of the Acoustical Society of America*, vol. 107, no. 6, p. 3438–3451, Jun. 2000.

[4] J. C. Krause and L. D. Braida, "Acoustic properties of naturally produced clear speech at normal speaking rates," *The Journal of the Acoustical Society of America*, vol. 115, no. 1, p. 362–378, Jan. 2004.

[5] F. Kelly and J. H. Hansen, "Analysis and calibration of lombard effect and whisper for speaker recognition," *IEEE/ACM Transactions on Audio, Speech, and Language Processing*, vol. 29, p. 927–942, 2021.

[6] J. G. Švec and S. Granqvist, "Tutorial and guidelines on measurement of sound pressure level in voice and speech," *Journal of Speech, Language, and Hearing Research*, vol. 61, no. 3, p. 441–461, Mar. 2018.

[7] J.-S. Liénard, "Quantifying vocal effort from the shape of the one-third octave long-term-average spectrum of speech," *The Journal of the Acoustical Society of America*, vol. 146, no. 4, pp. EL369–EL375, 2019.

[8] C. d'Alessandro, *Voice source parameters and prosodic analysis*. Berlin: Walter de Gruyter, 2006, p. 63–87.

[9] I. R. Titze and J. Sundberg, "Vocal intensity in speakers and singers," *The Journal of the Acoustical Society of America*, vol. 91, no. 5, p. 2936–2946, May 1992.

[10] A. Rilliard, C. d'Alessandro, and M. Evrard, "Paradigmatic variation of vowels in expressive speech: Acoustic description and dimensional analysis," *The Journal of the Acoustical Society of America*, vol. 143, no. 1, p. 109–122, Jan. 2018.

[11] P. Zelinka, M. Sigmund, and J. Schimmel, "Impact of vocal effort variability on automatic speech recognition," *Speech Communication*, vol. 54, no. 6, p. 732–742, Jul. 2012.

[12] J.-S. Liénard and C. Barras, "Fine-grain voice strength estimation from vowel spectral cues," in *Proceedings of Interspeech 2013, 14th Annual Conference of the International Speech Communication Association, Lyon, France*, 2013, p. 128–132.

[13] M. Goudebeek and K. Scherer, "Beyond arousal: Valence and potency/control cues in the vocal expression of emotion," *The Journal of the Acoustical Society of America*, vol. 128, no. 3, p. 1322–1336, 2010.

[14] J. F. Brandt, K. F. Ruder, and T. Shipp, "Vocal loudness and effort in continuous speech," *The Journal of the Acoustical Society of America*, vol. 46, no. 6B, p. 1543–1548, Dec. 1969.

[15] R. O. Sikveland, "Failed summons: Phonetic features of persistence and intensification in crisis negotiation," *Journal of Pragmatics*, vol. 150, p. 167–179, Sep. 2019.

[16] E. Székely, J. Mendelson, and J. Gustafson, "Synthesising uncertainty: The interplay of vocal effort and hesitation disfluencies." in *Interspeech*, 2017, pp. 804–808.

[17] E. Ford Baldner, E. Doll, and M. R. Van Mersbergen, "A review of measures of vocal effort with a preliminary study on the establishment of a vocal effort measure," *Journal of Voice*, vol. 29, no. 5, p. 530–541, Sep. 2015.

[18] A. L. Rosenthal, S. Y. Lowell, and R. H. Colton, "Aerodynamic and acoustic features of vocal effort," *Journal of Voice*, vol. 28, no. 2, p. 144–153, Mar. 2014.

[19] E. J. Hunter, L. C. Cantor-Cutiva, E. Van Leer, M. Van Mersbergen, C. D. Nanjundeswaran, P. Bottalico, M. J. Sandage, and S. Whitling, "Toward a consensus description of vocal effort, vocal load, vocal loading, and vocal fatigue," *Journal of Speech, Language, and Hearing Research*, vol. 63, no. 2, p. 509–532, Feb. 2020.

[20] P. Alku, M. Kodali, L. Laaksonen, and S. R. Kadiri, "Avid: A speech database for machine learning studies on vocal intensity," *Speech Communication*, vol. 157, p. 103039, 2024.

[21] M. Evrard, "Synthèse de parole expressive à partir du texte : Des phonostyles au contrôle gestuel pour la synthèse paramétrique statistique," PhD Thesis, Université Paris Sud, 2015. [Online]. Available: http://www.theses.fr/2015PA112202

[22] R. Banse and K. R. Scherer, "Acoustic profiles in vocal emotion expression," *Journal of Personality and Social Psychology*, vol. 70, p. 614–636, 1996.

[23] P. Alku, T. Bäckström, and E. Vilkman, "Normalized amplitude quotient for parametrization of the glottal flow," *The Journal of the Acoustical Society of America*, vol. 112, no. 2, p. 701–710, Aug. 2002.

[24] J. Kane and C. Gobl, "Identifying regions of non-modal phonation using features of the wavelet transform," in *Interspeech 2011. ISCA, Aug. 2011*, p. 177–180. [Online]. Available: https://www.isca-speech.org/archive/interspeech_2011/kane11_interspeech.html

[25] M. Kodali, S. Kadiri, and P. Alku, "Fine-tuning of pre-trained models for classification of vocal intensity category from speech signals," in *Interspeech. International Speech Communication Association (ISCA)*, 2024, pp. 482–486.

[26] A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, "wav2vec 2.0: A framework for self-supervised learning of speech representations," *Advances in neural information processing systems*, vol. 33, pp. 12449–12460, 2020.

[27] L. Ferrer and P. Riera, "Confidence intervals for evaluation in machine learning," https://github.com/luferrer/ConfidenceIntervals, 2024. [Online]. Available: https://github.com/luferrer/ConfidenceIntervals

[28] C. Gobl and A. Ní Chasaide, "The role of voice quality in communicating emotion, mood and attitude," *Speech Communication*, vol. 40, no. 1–2, p. 189–212, Apr. 2003.