Efficient real-time noise estimation without explicit speech, non-speech detection: an assessment on the AURORA corpus

This paper addresses the problem of noise estimation for speech enhancement and automatic speech recognition. In the context of mobile telephony, there is a requirement for low resource algorithms which must run at real-time. This paper describes the implementation of a previously published approach, termed quantile-based noise estimation, integrated within a conventional spectral subtraction framework. The novelty lies in the efficiency of the noise estimation process. Assessment is carried out on the AURORA corpus and demonstrates significant improvements in efficiency. Automatic speech recognition results show an average relative improvement of 26% over the baseline.