Histogram-Based Quantization for Robust and/or Distributed Speech Recognition

Abstract--In a distributed speech recognition (DSR) framework, the speech features are quantized and compressed at the client and recognized at the server. However, recognition accuracy is degraded by environmental noise at the input, quantization distortion, and transmission errors. In this paper, histogram-based quantization (HQ) is proposed, in which the partition cells for quantization are dynamically defined by the histogram or order statistics of a segment of the most recent past values of the parameter to be quantized. This scheme is shown to be able to solve to a good degree many problems related to DSR. A joint uncertainty decoding (JUD) approach is further developed to consider the uncertainty caused by both environmental noise and quantization errors. A three-stage error concealment (EC) framework is also developed to handle transmission errors. The proposed HQ is shown to be an attractive feature transformation approach for robust speech recognition outside of a DSR environ...

• Type: Journal
• Year: 2008
• Where: TASLP
• Authors: Chia-Yu Wan, Lin-Shan Lee
• Keywords: Environmental Noise, Speech Recognition, TASLP 2008, Transmission Errors