Joint Uncertainty Decoding (JUD) with Histogram-Based Quantization (HQ) for Robust and/or Distributed Speech Recognition

Histogram-based quantization (HQ) has been recently proposed as a robust and scalable quantization approach for distributed speech recognition (DSR). In this paper, histogram-based quantization (HQ) is further verified as an attractive feature transformation approach for robust speech recognition, joint uncertainty decoding (JUD) is developed to be applied with HQ for improved recognition accuracy, and the approach was evaluated for both cases of robust speech recognition and DSR. In joint uncertainty decoding (JUD), we jointly consider and estimate the uncertainty caused by both the environmental noise and the quantization errors in Viterbi decoding under the framework of HQ. For robust speech recognition, HQ was used as the front-end feature transformation and JUD as the enhancement approach at the back-end recognizer. For DSR, HQ was applied at the client end as a data compression process and JUD at the server. The evaluation with Aurora 2.0 testing environment showed very significant improvements for both cases of robust and/or distributed speech recognition