A novel approach to noise estimation in model-based speech feature enhancement
Konferenz: Sprachkommunikation 2008 - 8. ITG-Fachtagung
08.10.2008 - 10.10.2008 in Aachen, Germany
Tagungsband: Sprachkommunikation 2008
Seiten: 4Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Windmann, Stefan; Haeb-Umbach, Reinhold (Dept. of Communications Engineering, University of Paderborn, 33098 Paderborn, Germany)
In this paper, the noise estimation for model-based speech feature enhancement in automatic speech recognition (ASR) is investigated. Beside a stationary noise prior, three linear state space models for the (cepstral) noise process are considered. We have derived novel EM algorithms for the estimation of the noise model parameters: A blockwise EM algorithm is applied on noise-only input data. It is supposed to be used during the offline training mode of the recognizer. Further a sequential online EM algorithm is employed to adapt the observation variance in recognition mode which works as well under the asumption of a stationary noise prior and a linear state model for the noise. Experiments on the AURORA4 database lead to improved recognition results with the new state model compared to the assumption of stationary noise.