Tunable Q-factor Wavelet Transform-Based Features in the Classification of Phonation Types in the Singing and Speaking Voice.

Phonation is the use of the laryngeal system, with the help of an air-stream provided by the respiratory system, to generate audible sounds. Humans are capable of generating voices of various phonation types (eg, breathy, neutral, and pressed), and these types are used both in singing and speaking. In this study, we propose to use features derived using the tunable Q-factor wavelet transform (TQWT) for classification of phonation types in the singing and speaking voice.

Can a Machine Distinguish High and Low Amount of Social Creak in Speech?

Objectives: Increased prevalence of social creak particularly among female speakers has been reported in several studies. The study of social creak has been previously conducted by combining perceptual evaluation of speech with conventional acoustical parameters such as the harmonic-to-noise ratio and cepstral peak prominence. In the current study, machine learning (ML) was used to automatically distinguish speech of low amount of social creak from speech of high amount of social creak.

Effect of Vocalization on Human Aerosol Dynamics: Whispering Produces More Aerosols than Speaking.

Objectives: Sound pressure and exhaled flow have been identified as important factors associated with higher particle emissions. The aim of this study was to assess how different vocalizations affect the particle generation independently from other factors.

Design: Experimental study.

Classification of phonation types in singing voice using wavelet scattering network-based features.

The automatic classification of phonation types in singing voice is essential for tasks such as identification of singing style. In this study, it is proposed to use wavelet scattering network (WSN)-based features for classification of phonation types in singing voice. WSN, which has a close similarity with auditory physiological models, generates acoustic features that greatly characterize the information related to pitch, formants, and timbre.

Exploring the Impact of Fine-Tuning the Wav2vec2 Model in Database-Independent Detection of Dysarthric Speech.

Many acoustic features and machine learning models have been studied to build automatic detection systems to distinguish dysarthric speech from healthy speech. These systems can help to improve the reliability of diagnosis. However, speech recorded for diagnosis in real-life clinical conditions can differ from the training data of the detection system in terms of, for example, recording conditions, speaker identity, and language.