A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech

Investigating the phonatory processes in connected speech from high-speed videoendoscopy (HSV) demands the accurate detection of the vocal fold edges during vibration. The present paper proposes a new spatio-temporal technique to automatically segment vocal fold edges in HSV data during running speech. The HSV data were recorded from a vocally normal adult during a reading of the “Rainbow Passage.” The introduced technique was based on an unsupervised machine-learning (ML) approach combined with an active contour modeling (ACM) technique (also known as a hybrid approach). The hybrid method was implemented to capture the edges of vocal folds on different HSV kymograms, extracted at various cross-sections of vocal folds during vibration. The k-means clustering method, an ML approach, was first applied to cluster the kymograms to identify the clustered glottal area and consequently provided an initialized contour for the ACM. The ACM algorithm was then used to precisely detect the glottal edges of the vibrating vocal folds. The developed algorithm was able to accurately track the vocal fold edges across frames with low computational cost and high robustness against image noise. This algorithm offers a fully automated tool for analyzing the vibratory features of vocal folds in connected speech

Measuring vocal quality with speech synthesis.

Much previous research has demonstrated that listeners do not agree well when using traditional rating scales to measure pathological voice quality. Although these findings may indicate that listeners are inherently unable to agree in their perception of such complex auditory stimuli, another explanation implicates the particular measurement method-rating scale judgments-as the culprit. An alternative method of assessing quality-listener-mediated analysis-synthesis-was devised to assess this possibility. In this new approach, listeners explicitly compare synthetic and natural voice samples, and adjust speech synthesizer parameters to create auditory matches to voice stimuli. This method is designed to replace unstable internal standards for qualities like breathiness and roughness with externally presented stimuli, to overcome major hypothetical sources of disagreement in rating scale judgments. In a preliminary test of the reliability of this method, listeners were asked to adjust the signal-to-noise ratio for 12 synthetic pathological voices so that the resulting stimuli matched the natural target voices as well as possible For comparison to the synthesis judgments, listeners also judged the noisiness of the natural stimuli in a separate task using a traditional visual-analog rating scale. For 9 of the 12 voices, agreement among listeners was significantly (and substantially) greater for the synthesis task than for the rating scale task. Response variances for the two tasks did not differ for the remaining three voices. However, a second experiment showed that the synthesis settings that listeners selected for these three voices were within a difference limen, and therefore observed differences were perceptually insignificant. These results indicate that listeners can in fact agree in their perceptual assessments of voice quality, and that analysis-synthesis can measure perception reliably