Iterative speaker adaptation for speech recognition

Abstract

A speaker-independent speech recognition system is desirable in many applications where speaker-specific data does not exist. It speaker-independent data is available, the system could be adapted to the specific speaker, thereby reducing the recognition error rate. A new, unsupervised speaker adaptation scheme which requires no prior training phase is proposed. The algorithm improves the recognition rate as more speech data becomes available, making it most suitable for real-time implementation. In the tests conducted this algorithm yields an improvement of almost 50% on the recognition error rate.<>