Assessing local noise level estimation methods: Application to noise robust ASR

In this paper, we assess and compare four methods for the local estimation of noise spectra, namely the energy clustering, the Hirsch histograms, the weighted average method and the low-energy envelope tracking. Moreover we introduce, for these four approaches, the harmonic filtering strategy, a new pre-processing technique, expected to better track fast modulations of the noise energy. The speech periodicity property is used to update the noise level estimate during voiced parts of speech, without explicit detection of voiced portions. Our evaluation is performed with six different kinds of noises (both artificial and real noises) added to clean speech. The best noise level estimation method is then applied to noise robust speech recognition based on techniques requiring a dynamic estimation of the noise spectra, namely spectral subtraction and missing data compensation. (C) 2001 Elsevier Science B.V. All rights reserved.