A new algorithm for robust speech recognition: the delta vector taylor series approach

In this paper we present a new model-based compensation technique called Delta Vector Taylor Series (DVTS). This new technique is an extension and improvement over the Vector Taylor Series (VTS) approach [7] that addresses several of its limitations. In particular, we present a new statistical representation for the distribution of clean speech feature vectors based on a weighted vector codebook. This change to the underlying probability density function (PDF) allows us to produce more accurate and stable solutions for our algorithm. The algorithm is also presented in a EM-MAP framework where some the environmental parameters are treated as random variables with known PDF's. Finally, we explore a new compensation approach based on the use of convex hulls. We evaluate our algorithm in a phonetic classi cation task on the TIMIT [5] database and also in a small vocabulary size speech recognition database. In both databases arti cial and natural noise is injected at several signal to noise ratios (SNR). The algorithm achieves matched performance at all SNR's above 10 dB.