Linear spectral transformation for robust speech recognition using maximum mutual information

This paper presents a transformation-based rapid adaptation technique for robust speech recognition using a linear spectral transformation (LST) and a maximum mutual information (NIMI) criterion. Previously, a maximum likelihood linear spectral transformation (ML-LST) algorithm was proposed for fast adaptation in unknown environments. Since the NIMI estimation methods does not require evenly distributed training data and increases the a posteriori probability of the word sequences of the training data, we combine the linear spectral transformation method and the MMI estimation technique in order to achieve extremely rapid adaptation using only one word of adaptation data. The proposed algorithm, called MMI-LST, was implemented using the extended Baum-Welch algorithm and phonetic lattices, and evaluated on the TIMIT and FFMTIMIT corpora. It provides a relative reducion in the speech recognition error rate of 11.1% using only 0.25 s of adaptation data.