Robust speech/non-speech discrimination based on pitch estimation for mobile robots

Abstract

To be used on a mobile robot, speech/non-speech discrimination must be robust to environmental noise and to the position of the interlocutor, without necessarily having to satisfy low-latency requirements. To address these conditions, this paper presents a speech/non-speech discrimination approach based on pitch estimation. Pitch features are robust to noise and reverberation, and can be estimated over a few seconds. Results suggest that our approach is more robust compared to the use of Mel-Frequency Cepstrum Coefficients with Gaussian Mixture Models (MFCC-GMM) under high reverberation levels and additive noise (with an accuracy above 98% with a latency of 2.21 sec), which makes it ideal for mobile robot applications. The approach is also validated on a mobile robot equipped with a 8-microphone array, using speech/non-speech discrimination based on pitch estimation as a post-processing module of a localization, tracking and separation system.