Using speech/non-speech detection to bias recognition search on noisy data

Abstract

This paper focuses on the recognition of noisy speech. We show that the decoding of a noisy speech waveform can be facilitated if the recognizer has explicit knowledge of where it should hypothesize speech phones, and where it should map the acoustics to non-speech phones. We build a speech/non-speech detector and use its output as an additional front-end feature. We show that by appropriately weighting the contribution of this feature in the decoder and by modifying the acoustic models accordingly, we can penalize speech/non-speech confusions and consequently reduce the recognition error rate. This approach gives a 12% overall error rate reduction on a wide variety of recognition tasks and noise characteristics without degrading performance on clean test data. A simple extension of the approach boosts recognition improvements on noisy test sets to 14% overall.