The JIBO Kids Corpus: A speech dataset of child-robot interactions in a classroom environment.

This paper describes an original dataset of children's speech, collected through the use of JIBO, a social robot. The dataset encompasses recordings from 110 children, aged 4-7 years old, who participated in a letter and digit identification task and extended oral discourse tasks requiring explanation skills, totaling 21 h of session data. Spanning a 2-year collection period, this dataset contains a longitudinal component with a subset of participants returning for repeat recordings.

Unraveling the Associations Between Voice Pitch and Major Depressive Disorder: A Multisite Genetic Study.

Major depressive disorder (MDD) often goes undiagnosed due to the absence of clear biomarkers. We sought to identify voice biomarkers for MDD and separate biomarkers indicative of MDD predisposition from biomarkers reflecting current depressive symptoms. Using a two-stage meta-analytic design to remove confounds, we tested the association between features representing vocal pitch and MDD in a multisite case-control cohort study of Chinese women with recurrent depression.

MKELM based multi-classification model for foreign accent identification.

The automatic identification of foreign accents can play a crucial role in various speech systems, including speaker identification, e-learning, telephone banking, and more. Additionally, it can greatly enhance the robustness of Automatic Speech Recognition (ASR) systems. Non-native accents in speech signals are characterized by distinct pronunciations, prosody, and voice characteristics of the speaker.

Genetic association analysis of human median voice pitch identifies a common locus for tonal and non-tonal languages.

The genetic influence on human vocal pitch in tonal and non-tonal languages remains largely unknown. In tonal languages, such as Mandarin Chinese, pitch changes differentiate word meanings, whereas in non-tonal languages, such as Icelandic, pitch is used to convey intonation. We addressed this question by searching for genetic associations with interindividual variation in median pitch in a Chinese major depression case-control cohort and compared our results with a genome-wide association study from Iceland.

An exploratory study on dialect density estimation for children and adult's African American Englisha).

This paper evaluates an innovative framework for spoken dialect density prediction on children's and adults' African American English. A speaker's dialect density is defined as the frequency with which dialect-specific language characteristics occur in their speech. Rather than treating the presence or absence of a target dialect in a user's speech as a binary decision, instead, a classifier is trained to predict the level of dialect density to provide a higher degree of specificity in downstream tasks.

A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from Speech.

The proposed method focuses on speaker disentanglement in the context of depression detection from speech signals. Previous approaches require patient/speaker labels, encounter instability due to loss maximization, and introduce unnecessary parameters for adversarial domain prediction. In contrast, the proposed unsupervised approach reduces cosine similarity between latent spaces of depression and pre-trained speaker classification models.

Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement.

Speech signals are valuable biomarkers for assessing an individual's mental health, including identifying Major Depressive Disorder (MDD) automatically. A frequently used approach in this regard is to employ features related to speaker identity, such as speaker-embeddings. However, over-reliance on speaker identity features in mental health screening systems can compromise patient privacy.

Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals.

While speech-based depression detection methods that use speaker-identity features, such as speaker embeddings, are popular, they often compromise patient privacy. To address this issue, we propose a speaker disentanglement method that utilizes a non-uniform mechanism of adversarial SID loss maximization. This is achieved by varying the adversarial weight between different layers of a model during training.

A Step Towards Preserving Speakers' Identity While Detecting Depression Via Speaker Disentanglement.

Preserving a patient's identity is a challenge for automatic, speech-based diagnosis of mental health disorders. In this paper, we address this issue by proposing adversarial disentanglement of depression characteristics and speaker identity. The model used for depression classification is trained in a speaker-identity-invariant manner by minimizing depression prediction loss and maximizing speaker prediction loss during training.

Unsupervised Instance Discriminative Learning for Depression Detection from Speech Signals.

Major Depressive Disorder (MDD) is a severe illness that affects millions of people, and it is critical to diagnose this disorder as early as possible. Detecting depression from voice signals can be of great help to physicians and can be done without any invasive procedure. Since relevant labelled data are scarce, we propose a modified Instance Discriminative Learning (IDL) method, an unsupervised pre-training technique, to extract augment-invariant and instance-spread-out embeddings.

FRAUG: A FRAME RATE BASED DATA AUGMENTATION METHOD FOR DEPRESSION DETECTION FROM SPEECH SIGNALS.

In this paper, a data augmentation method is proposed for depression detection from speech signals. Samples for data augmentation were created by changing the frame-width and the frame-shift parameters during the feature extraction process. Unlike other data augmentation methods (such as VTLP, pitch perturbation, or speed perturbation), the proposed method does not explicitly change acoustic parameters but rather the time-frequency resolution of frame-level features.

Speaker discrimination performance for "easy" versus "hard" voices in style-matched and -mismatched speech.

This study compares human speaker discrimination performance for read speech versus casual conversations and explores differences between unfamiliar voices that are "easy" versus "hard" to "tell together" versus "tell apart." Thirty listeners were asked whether pairs of short style-matched or -mismatched, text-independent utterances represented the same or different speakers. Listeners performed better when stimuli were style-matched, particularly in read speech-read speech trials (equal error rate, EER, of 6.

Subglottal resonances of American English speaking children.

This paper presents an investigation of children's subglottal resonances (SGRs), the natural frequencies of the tracheo-bronchial acoustic system. A total of 43 children (31 male, 12 female) aged between 6 and 18 yr were recorded. Both microphone signals of various consonant-vowel-consonant words and subglottal accelerometer signals of the sustained vowel /ɑ/ were recorded for each of the children, along with age and standing height.

Towards understanding speaker discrimination abilities in humans and machines for text-independent short utterances of different speech styles.

Little is known about human and machine speaker discrimination ability when utterances are very short and the speaking style is variable. This study compares text-independent speaker discrimination ability of humans and machines based on utterances shorter than 2 s in two different speaking styles (read sentences and speech directed towards pets, characterized by exaggerated prosody). Recordings of 50 female speakers drawn from the UCLA Speaker Variability Database were used as stimuli.

Robust speaker identification via fusion of subglottal resonances and cepstral features.

This letter investigates the use of subglottal resonances (SGRs) for noise-robust speaker identification (SID). It is motivated by the speaker specificity and stationarity of subglottal acoustics, and the development of noise-robust SGR estimation algorithms which are reliable at low signal-to-noise ratios for large datasets. A two-stage framework is proposed which combines the SGRs with different cepstral features.

A robust automatic birdsong phrase classification: A template-based approach.

Automatic phrase detection systems of bird sounds are useful in several applications as they reduce the need for manual annotations. However, birdphrase detection is challenging due to limited training data and background noise. Limited data occur because of limited recordings or the existence of rare phrases.

Perceptual evaluation of voice source models.

Models of the voice source differ in their fits to natural voices, but it is unclear which differences in fit are perceptually salient. This study examined the relationship between the fit of five voice source models to 40 natural voices, and the degree of perceptual match among stimuli synthesized with each of the modeled sources. Listeners completed a visual sort-and-rate task to compare versions of each voice created with the different source models, and the results were analyzed using multidimensional scaling.

Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training data.

Annotation of phrases in birdsongs can be helpful to behavioral and population studies. To reduce the need for manual annotation, an automated birdsong phrase classification algorithm for limited data is developed. Limited data occur because of limited recordings or the existence of rare phrases.

The glottaltopogram: a method of analyzing high-speed images of the vocal folds.

Laryngeal high-speed videoendoscopy is a state-of-the-art technique to examine physiological vibrational patterns of the vocal folds. With sampling rates of thousands of frames per second, high-speed videoendoscopy produces a large amount of data that is difficult to analyze subjectively. In order to visualize high-speed video in a straightforward and intuitive way, many methods have been proposed to condense the three-dimensional data into a few static images that preserve characteristics of the underlying vocal fold vibratory patterns.

Development of a glottal area index that integrates glottal gap size and open quotient.

Because voice signals result from vocal fold vibration, perceptually meaningful vibratory measures should quantify those aspects of vibration that correspond to differences in voice quality. In this study, glottal area waveforms were extracted from high-speed videoendoscopy of the vocal folds. Principal component analysis was applied to these waveforms to investigate the factors that vary with voice quality.

Variability in the relationships among voice quality, harmonic amplitudes, open quotient, and glottal area waveform shape in sustained phonation.

Increases in open quotient are widely assumed to cause changes in the amplitude of the first harmonic relative to the second (H1*-H2*), which in turn correspond to increases in perceived vocal breathiness. Empirical support for these assumptions is rather limited, and reported relationships among these three descriptive levels have been variable. This study examined the empirical relationship among H1*-H2*, the glottal open quotient (OQ), and glottal area waveform skewness, measured synchronously from audio recordings and high-speed video images of the larynges of six phonetically knowledgeable, vocally healthy speakers who varied fundamental frequency and voice qualities quasi-orthogonally.

Subglottal resonances of adult male and female native speakers of American English.

This paper presents a large-scale study of subglottal resonances (SGRs) (the resonant frequencies of the tracheo-bronchial tree) and their relations to various acoustical and physiological characteristics of speakers. The paper presents data from a corpus of simultaneous microphone and accelerometer recordings of consonant-vowel-consonant (CVC) words embedded in a carrier phrase spoken by 25 male and 25 female native speakers of American English ranging in age from 18 to 24 yr. The corpus contains 17,500 utterances of 14 American English monophthongs, diphthongs, and the rhotic approximant [[inverted r]] in various CVC contexts.

Resonances and wave propagation velocity in the subglottal airways.

Previous studies of subglottal resonances have reported findings based on relatively few subjects, and the relations between these resonances, subglottal anatomy, and models of subglottal acoustics are not well understood. In this study, accelerometer signals of subglottal acoustics recorded during sustained [a:] vowels of 50 adult native speakers (25 males, 25 females) of American English were analyzed. The study confirms that a simple uniform tube model of subglottal airways, closed at the glottis and open at the inferior end, is appropriate for describing subglottal resonances.