Automatic detection of nasalization in speech via cue-based analysis

Modern speech recognition systems are powerful, but it is not simple to understand the mechanisms by which they model speech, or precisely how they make use of acoustic cues. Moreover, a system for the explicit detection of individual acoustic cues may be useful not only for speech recognition but for understanding the principles that govern sub-phonemic patterns of surface-phonetic variation, and how specific cue patterns may reflect speech disorders. This report describes a transparent, acoustic cue-based nasalization detection module, that can be used to find not only nasal consonants but also nasalization wherever it appears, e.g., within a vocalic region. We lay out a framework for extracting measurements that are key to detecting nasalization, in particular, the nasality-related spectral peaks P0 and P1, and confirm the utility of these measurements for nasality detection in spoken words. In particular, we present a pre-processing and cue-value extraction framework, and propose a Gaussian Mixture Model-based approach to detect the regions of nasalization in a speech signal. This work on a detection module for nasalization is part of a larger effort to develop a speech recognition system that is based on landmarks and other acoustic cues.