Automated detection of Glottal-related acoustic cues for feature-cue-based analysis

Abstract

Detecting and interpreting individual acoustic cues to identify features that distinguish among speech sounds is thought to play a key role in automatic speech processing, modeling human speech perception, detecting and diagnosing speech disabilities (and tracking the effects of treatment for those disabilities), and studying cross-language differences. The individual acoustic cues form a bridge between transcription using phonemic symbols (phonemes) and raw acoustic measurements from the signal and allow the analysis to capture systematic language-related differences in how words are pronounced in different contexts–differences that are critical to speech recognition. Within this framework, analysis of glottal-related acoustic cues, such as aspiration and voicing, can contribute to the identification of phonemes that exhibit these acoustic cues in their respective productions. In this study, we propose an algorithm that uses time-series data processing methods and machine learning models to accurately predict the presence of the glottal cue labels from waveform analysis. We further highlight the accuracy of our model and cue-based labeling method in identifying speech modifications over phone and phoneme-based methods.