A noninvasive technique for detecting hypernasal speech using a nonlinear operator

Abstract

Speakers with a defective velopharyngeal mechanism produce speech with inappropriate nasal resonance (hypernasal speech). It is of clinical interest to detect hypernasality as it is indicative of an anatomical, neurological, or peripheral nervous system problem. There are various clinical techniques used to determine hypernasality. The current techniques are physically invasive or intrusive to some extent. A preferred approach for detecting hypernasality, would be noninvasive to maximize patient comfort and naturalness of speaking. In this study, a noninvasive technique based on the Teager Energy operator is proposed. Utilizing a property of the Teager Energy operator and a model for normal and nasalized speech, a significant difference between the Teager Energy profile for lowpass and bandpass filtered nasalized speech is shown. This difference is shown to be nonexistent for normal speech. A classification algorithm is formulated that detects the presence of hypernasality using a measure of the difference in the Teager Energy profiles. The classification algorithm was evaluated using a native English speaker population producing front (/i/) and mid (/A/) vowels. Results show that the presence of hypernasality in speech can be reliably detected using the proposed classification algorithm.