Three-Stage Error Concealment for Distributed Speech Recognition (DSR) with Histogram-Based Quantization (HQ) Under Noisy Environment

In this paper, a three-stage error concealment (EC) framework based on the recently proposed histogram-based quantization (HQ) for distributed speech recognition (DSR) is proposed, in which noisy input speech is assumed and both the transmission errors and environmental noise are considered jointly. The first stage detects the erroneous feature parameters at both the frame and subvector levels. The second stage then reconstructs the detected erroneous subvectors by MAP estimation, considering the prior speech source statistics, the channel transition probability, and the reliability of the received subvectors. The third stage then considers the uncertainty of the estimated vectors during Viterbi decoding. At each stage, the error concealment (EC) techniques properly exploit the inherent robust nature of histogram-based quantization (HQ). Extensive experiments with AURORA 2.0 testing environment and GPRS simulation indicated the proposed framework is able to offer significantly improved performance against a wide variety of environmental noise and transmission error conditions.