An Analytic Modeling Approach to Enhancing Throat Microphone Speech Commands for Keyword Spotting

摘要

This research was carried out on enhancing throat microphone speech for noise-robust speech keyword spotting. The enhancement was performed by mapping the log-energy in the Mel-frequency bands of throat microphone speech to those of the corresponding close-talk microphone speech. An analytic equation detection system, Eureqa, which can infer nonlinear relations directly from observed data, was used to identify the enhancement models. Speech recognition experiments with the enhanced throat microphone speech keywords indicate that the analytic enhancement models performed well in terms of recognition accuracy. Unvoiced consonants, however, could not be enhanced well enough, mostly because they were not effectively recorded by the throat microphone.