Detection of Acoustic-Phonetic Landmarks in Mismatched Conditions using a Biomimetic Model of Human Auditory Processing

Acoustic-phonetic landmarks provide robust cues for speech recognition and are relatively invariant between speakers, speaking styles, noise conditions and sampling rates. The ability to detect acoustic-phonetic landmarks as a front-end for speech recognition has been shown to improve recognition accuracy. Biomimetic inter-spike intervals and average signal level have been shown to accurately convey information about acoustic-phonetic landmarks. This paper explores the use of inter-spike interval and average signal level as input features for landmark detectors trained and tested on mismatched conditions. These detectors are designed to serve as a front-end for speech recognition systems. Results indicate that landmark detectors trained using inter-spike intervals and signal level are relatively robust to both additive channel noise and changes in sampling rate.