分布式语音识别系统中基于奇异值分解的MFCC压缩方案

A. Touazi, M. Debyeche
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引用次数: 1

摘要

提出了一种分布式语音识别(DSR)系统中低频倒谱系数(MFCCs)的低比特率源编码方案。该方法将压缩后的ETSI高级前端(ETSI- afe)特征分解为SVD分量。通过研究连续MFCC帧之间的相关性,采用ETSI-AFE对奇数帧进行编码,而对偶数帧只编码并传输奇异值和最接近的左奇异向量索引。在服务器端,通过量化奇异值和最接近的左奇异向量来评估非传输mfc。系统提供2.7 kbps的压缩比特率。在Aurora-2数据库上进行了清洁和多条件训练模式的识别实验。仿真结果表明,相对于ETSI-AFE编码器,该算法具有良好的识别性能,且没有明显的退化。
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An SVD-based scheme for MFCC compression in distributed speech recognition system
This paper proposes a new scheme for low bit-rate source coding of Mel Frequency Cepstral Coefficients (MFCCs) in Distributed Speech Recognition (DSR) system. The method uses the compressed ETSI Advanced Front-End (ETSI-AFE) features factorized into SVD components. By investigating the correlation property between successive MFCC frames, the odd ones are encoded using ETSI-AFE, while only the singular values and the nearest left singular vectors index are encoded and transmitted for the even frames. At the server side, the non-transmitted MFCCs are evaluated through their quantized singular values and the nearest left singular vectors. The system provides a compression bit-rate of 2.7 kbps. The recognition experiments were carried out on the Aurora-2 database for clean and multi-condition training modes. The simulation results show good recognition performance without significant degradation, with respect to the ETSI-AFE encoder.
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