M. Borsky, D. D. Mehta, J. H. Van Stan, and J. Gudnason, “Modal and nonmodal voice quality classification using acoustic and electroglottographic features,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 12, pp. 2281-2291, 2017. Publisher's VersionAbstract
    The goal of this study was to investigate the performance of different feature types for voice quality classification using multiple classifiers. The study compared the COVAREP feature set; which included glottal source features, frequency warped cepstrum, and harmonic model features; against the mel-frequency cepstral coefficients (MFCCs) computed from the acoustic voice signal, acoustic-based glottal inverse filtered (GIF) waveform, and electroglottographic (EGG) waveform. Our hypothesis was that MFCCs can capture the perceived voice quality from either of these three voice signals. Experiments were carried out on recordings from 28 participants with normal vocal status who were prompted to sustain vowels with modal and nonmodal voice qualities. Recordings were rated by an expert listener using the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V), and the ratings were transformed into a dichotomous label (presence or absence) for the prompted voice qualities of modal voice, breathiness, strain, and roughness. The classification was done using support vector machines, random forests, deep neural networks, and Gaussian mixture model classifiers, which were built as speaker independent using a leave-one-speaker-out strategy. The best classification accuracy of 79.97% was achieved for the full COVAREP set. The harmonic model features were the best performing subset, with 78.47% accuracy, and the static+dynamic MFCCs scored at 74.52%. A closer analysis showed that MFCC and dynamic MFCC features were able to classify modal, breathy, and strained voice quality dimensions from the acoustic and GIF waveforms. Reduced classification performance was exhibited by the EGG waveform.
    J. H. Van Stan, D. D. Mehta, and R. E. Hillman, “Recent innovations in voice assessment expected to impact the clinical management of voice disorders,” Perspectives of the ASHA Special Interest Groups, vol. 2, no. SIG 3, pp. 4-13, 2017. Publisher's VersionAbstract

    This article provides a summary of some recent innovations in voice assessment expected to have an impact in the next 5–10 years on how patients with voice disorders are clinically managed by speech-language pathologists. Specific innovations discussed are in the areas of laryngeal imaging, ambulatory voice monitoring, and “big data” analysis using machine learning to produce new metrics for vocal health. Also discussed is the potential for using voice analysis to detect and monitor other health conditions.

    M. Borsky, M. Cocude, D. D. Mehta, M. Zañartu, and J. Gudnason, “Classification of voice modes using neck-surface accelerometer data,” in International Conference on Acoustics, Speech, and Signal Processing, 2017.Abstract


    This study analyzes signals recorded using a neck-surface accelerometer from subjects producing speech with different voice modes. The purpose is to explore if the recorded waveforms can capture the glottal vibratory patterns which can be related to the movement of the vocal folds and thus voice quality. The accelerometer waveforms do not contain the supraglottal resonances, and these characteristics make the proposed method suitable for real-life voice quality assessment and monitoring as it does not breach patient privacy. The experiments with a Gaussian mexture model classifier demonstrate that different voice qualities produce distinctly different accelerometer waveforms. The system achieved 80.2% and 89.5% for frame- and utterance-level accuracy, respectively, for classifying among modal, breathy, pressed, and rough voice modes using a speaker-dependent classifier. Finally, the article presents characteristic waveforms for each modality and discusses their attributes.


    M. Borsky, D. D. Mehta, J. P. Gudjohnsen, and J. Gudnason, “Classification of voice modality using electroglottogram waveforms,” in INTERSPEECH, 2016.Abstract


    It has been proven that the improper function of the vocal folds can result in perceptually distorted speech that is typically identified with various speech pathologies or even some neurological diseases. As a consequence, researchers have focused on finding quantitative voice characteristics to objectively assess and automatically detect non-modal voice types. The bulk of the research has focused on classifying the speech modality by using the features extracted from the speech signal. This paper proposes a different approach that focuses on analyzing the signal characteristics of the electroglottogram (EGG) waveform. The core idea is that modal and different kinds of non-modal voice types produce EGG signals that have distinct spectral/cepstral characteristics. As a consequence, they can be distinguished from each other by using standard cepstral-based features and a simple multivariate Gaussian mixture model. The practical usability of this approach has been verified in the task of classifying among modal, breathy, rough, pressed and soft voice types. We have achieved 83% frame-level accuracy and 91% utterance-level accuracy by training a speaker-dependent system.