Although laryngeal high-speed videoendoscopy (HSV) is crucial for studying vocal fold vibrations, its translation to clinical practice has been hindered by the large volume of data it produces and the difficulty in interpreting current analysis methods. Although image processing techniques have been developed to map spatial-temporal data into two-dimensional representations, they alter the geometrical construction of the glottis and do not provide standard quantitative features, thus challenging clinical interpretation. In response, we propose a new visualization and analysis framework for assessing the dynamics of vocal folds based on the empirical distribution of the glottal edge using HSV. This procedure analyzes vocal fold oscillations by preserving the shape of the glottis and quantifying the asymmetry between right and left vocal fold displacements along the anterior-posterior axis. This method was evaluated on four groups of participants: ten with normal voices, ten with vocal fold nodules, ten with muscle tension dysphonia, and two with unilateral vocal fold paralysis. The proposed method produces distinct representations for normal and pathological vocal fold vibratory behaviors and derived features based on amplitude and phase asymmetry metrics that show statistically significant differences between normal and pathological groups. Comparative analysis with state-of-the-art techniques indicates that our proposed method can complement the assessment of vocal fold vibration and enhance the clinical translation of HSV.

The use of non-invasive skin accelerometers placed over the extrathoracic trachea has been proposed in the literature for measuring vocal function. Glottal airflow is estimated using inverse filtering or Bayesian techniques based on a subglottal impedance-based model when utilizing these sensors. However, deviations in glottal airflow estimates can arise due to sensor positioning and model mismatch, and addressing them requires a significant computational load. In this paper, we utilize system identification techniques to obtain a low order state-space representation of the subglottal impedance-based model. We then employ the resulting low order model in a Kalman smoother to estimate the glottal airflow. Our proposed approach reduces the model order by 94% and requires only 1.5% of the computing time compared to previous Bayesian methods in the literature, while achieving slightly better accuracy when correcting for glottal airflow deviations. Additionally, our Kalman smoother approach provides a measure of uncertainty in the airflow estimate, which is valuable when measurements are taken under different conditions. With its comparable accuracy in signal estimation and reduced computational load, the proposed approach has the potential for real-time estimation of glottal airflow and its associated uncertainty in wearable voice ambulatory monitors using neck-surface acceleration.

Many voice disorders are the result of intricate neural and/or biomechanical impairments that are poorly understood. The limited knowledge of their etiological and pathophysiological mechanisms hampers effective clinical management. Behavioral studies have been used concurrently with computational models to better understand typical and pathological laryngeal motor control. Thus far, however, a unified computational framework that quantitatively integrates physiologically relevant models of phonation with the neural control of speech has not been developed. Here, we introduce LaDIVA, a novel neurocomputational model with physiologically based laryngeal motor control. We combined the DIVA model (an established neural network model of speech motor control) with the extended body-cover model (a physics-based vocal fold model). The resulting integrated model, LaDIVA, was validated by comparing its model simulations with behavioral responses to perturbations of auditory vocal fundamental frequency (fo) feedback in adults with typical speech. LaDIVA demonstrated capability to simulate different modes of laryngeal motor control, ranging from short-term (i.e., reflexive) and long-term (i.e., adaptive) auditory feedback paradigms, to generating prosodic contours in speech. Simulations showed that LaDIVA's laryngeal motor control displays properties of motor equivalence, i.e., LaDIVA could robustly generate compensatory responses to reflexive vocal fo perturbations with varying initial laryngeal muscle activation levels leading to the same output. The model can also generate prosodic contours for studying laryngeal motor control in running speech. LaDIVA can expand the understanding of the physiology of human phonation to enable, for the first time, the investigation of causal effects of neural motor control in the fine structure of the vocal signal.

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.

Glottal inverse filtering aims to estimate the glottal airflow signal from a speech signal for applications such as speaker recognition and clinical voice assessment. Nonetheless, evaluation of inverse filtering algorithms has been challenging due to the practical difficulties of directly measuring glottal airflow. Apart from this, it is acknowledged that the performance of many methods degrade in voice conditions that are of great interest, such as breathiness, high pitch, soft voice, and running speech. This paper presents a comprehensive, objective, and comparative evaluation of state-of-the-art inverse filtering algorithms that takes advantage of speech and glottal airflow signals generated by a physiological speech synthesizer. The synthesizer provides a physics-based simulation of the voice production process and thus an adequate test bed for revealing the temporal and spectral performance characteristics of each algorithm. Included in the synthetic data are continuous speech utterances and sustained vowels, which are produced with multiple voice qualities (pressed, slightly pressed, modal, slightly breathy, and breathy), fundamental frequencies, and subglottal pressures to simulate the natural variations in real speech. In evaluating the accuracy of a glottal flow estimate, multiple error measures are used, including an error in the estimated signal that measures overall waveform deviation, as well as an error in each of several clinically relevant features extracted from the glottal flow estimate. Waveform errors calculated from glottal flow estimation experiments exhibited mean values around 30% for sustained vowels, and around 40% for continuous speech, of the amplitude of true glottal flow derivative. Closed-phase approaches showed remarkable stability across different voice qualities and subglottal pressures. The algorithms of choice, as suggested by significance tests, are closed-phase covariance analysis for the analysis of sustained vowels, and sparse linear prediction for the analysis of continuous speech. Results of data subset analysis suggest that analysis of close rounded vowels is an additional challenge in glottal flow estimation.

Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, termed vocal hyperfunction. Thus an ongoing goal in clinical voice assessment is the long-term monitoring of noninvasively derived measures to track hyperfunction. This paper reports on a smartphone-based voice health monitor that records the high-bandwidth accelerometer signal from the neck skin above the collarbone. Data collection is under way from patients with vocal hyperfunction and matched-control subjects to create a dataset designed to identify the best set of diagnostic measures for hyperfunctional patterns of vocal behavior. Vocal status is tracked from neck acceleration using previously-developed vocal dose measures and novel model-based features of glottal airflow estimates. Clinically, the treatment of hyperfunctional disorders would be greatly enhanced by the ability to unobtrusively monitor and quantify detrimental behaviors and, ultimately, to provide real-time biofeedback that could facilitate healthier voice use.

A model-based inverse filtering scheme is proposed for an accurate, non-invasive estimation of the aerodynamic source of voiced sounds at the glottis. The approach, referred to as subglottal impedance-based inverse filtering (IBIF), takes as input the signal from a lightweight accelerometer placed on the skin over the extrathoracic trachea and yields estimates of glottal airflow and its time derivative, offering important advantages over traditional methods that deal with the supraglottal vocal tract. The proposed scheme is based on mechano-acoustic impedance representations from a physiologically-based transmission line model and a lumped skin surface representation. A subject-specific calibration protocol is used to account for individual adjustments of subglottal impedance parameters and mechanical properties of the skin. Preliminary results for sustained vowels with various voice qualities show that the subglottal IBIF scheme yields comparable estimates with respect to current aerodynamics-based methods of clinical vocal assessment. A mean absolute error of less than 10% was observed for two glottal airflow measures -maximum flow declination rate and amplitude of the modulation component- that have been associated with the pathophysiology of some common voice disorders caused by faulty and/or abusive patterns of vocal behavior (i.e., vocal hyperfunction). The proposed method further advances the ambulatory assessment of vocal function based on the neck acceleration signal, that previously have been limited to the estimation of phonation duration, loudness, and pitch. Subglottal IBIF is also suitable for other ambulatory applications in speech communication, in which further evaluation is underway.

Voice use is considered to play a major role in the development of many voice disorders, and clinicians focus on evaluating and modifying how patients use their voices throughout the day. Some voice monitoring devices have used neck-mounted accelerometers to unobtrusively and confidentially track voice use–related measures, such as phonation time, fundamental frequency, and sound intensity. Guidelines for the clinical use of such monitoring devices have yet to be established. This is a preliminary investigation into establishing initial benchmarks for obtaining robust estimates of long-term average voice use that may be used to begin examining basic relationships between vocal loading and voice use–related pathology. As expected, adequate monitoring durations depend on the inherent variability of the parameter of interest, with much of the error decreased after 26 hours of monitoring. Investigations are currently under way to take advantage of a smartphone-based voice monitoring system that is designed to enhance device wearability and enable the derivation of new clinically relevant measures. Index Terms: ambulatory voice monitoring, voice use, voice disorders, accelerometer 1.