Unified decoding and feature representation for improved speech recognition

In this paper we propose a unified framework for decoding and feature representation based on the Maximum A Posterior (MAP) principle. The search space is augmented with an additional feature stream dimension such that different feature representations can be utilized for different phonetic context under the HMM decoding framework. We also provide a theoretic explanation for the unified framework. It gives us “supervised” signal processing and feature extraction for the recognition system, which has reduced the word recognition error rate by 15% on a large-vocabulary continuous speech recognition task when multiple feature streams are used simultaneously.