7th International Conference on Spoken Language Processing

September 16-20, 2002
Denver, Colorado, USA

Robust Speech Recognition Using a Voiced-Unvoiced Feature

András Zolnay, Ralf Schlüter, Hermann Ney

RWTH Aachen - University of Technology, Germany

In this paper, a voiced-unvoiced measure is used as acoustic feature for continuous speech recognition. The voiced-unvoiced measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis (LDA) to choose the most relevant features. Experiments were performed on the SieTill (German digit strings recorded over telephone line) and on the SPINE (English spontaneous speech under different simulated noisy environments) corpus. The additional voiced-unvoiced measure results in improvements in word error rate (WER) of up to 11% relative to using MFCC alone with the same overall number of parameters in the system.


Full Paper

Bibliographic reference.  Zolnay, András / Schlüter, Ralf / Ney, Hermann (2002): "Robust speech recognition using a voiced-unvoiced feature", In ICSLP-2002, 1065-1068.