Quantization of Speech Features: Source Coding

  • Stephen So
  • Kuldip K. Paliwal
Part of the Advances in Pattern Recognition book series (ACVPR)

In this chapter, we describe various schemes for quantizing speech features to be used in distributed speech recognition (DSR) systems. We analyze the statistical properties of Mel frequency-warped cepstral coefficients (MFCCs) that are most relevant to quantization, namely the correlation and probability density function shape, in order to determine the type of quantization scheme that would be most suitable for quantizing them efficiently. We also determine empirically the relationship between mean squared error and recognition accuracy in order to verify that quantization schemes, which minimize mean squared error, are also guaranteed to improve the recognition performance. Furthermore, we highlight the importance of noise robustness in DSR and describe the use of a perceptually weighted distance measure to enhance spectral peaks in vector quantization. Finally, we present some experimental results on the quantization schemes in a DSR framework and compare their relative recognition performances.


Mean Square Error Discrete Cosine Transform Vector Quantizer Average Mean Square Error Quantization Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Stephen So
    • 1
  • Kuldip K. Paliwal
    • 1
  1. 1.Griffith School of Engineering, Signal Processing LaboratoryGriffith UniversityAustralia

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