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  • Research Article
  • Open Access

Objective Speech Quality Measurement Using Statistical Data Mining

EURASIP Journal on Advances in Signal Processing20052005:721258

  • Received: 7 November 2003
  • Published:


Measuring speech quality by machines overcomes two major drawbacks of subjective listening tests, their low speed and high cost. Real-time, accurate, and economical objective measurement of speech quality opens up a wide range of applications that cannot be supported with subjective listening tests. In this paper, we propose a statistical data mining approach to design objective speech quality measurement algorithms. A large pool of perceptual distortion features is extracted from the speech signal. We examine using classification and regression trees (CART) and multivariate adaptive regression splines (MARS), separately and jointly, to select the most salient features from the pool, and to construct good estimators of subjective listening quality based on the selected features. We show designs that use perceptually significant features and outperform the state-of-the-art objective measurement algorithm. The designed algorithms are computationally simple, making them suitable for real-time implementation. The proposed design method is scalable with the amount of learning data; thus, performance can be improved with more offline or online training.

Keywords and phrases

  • speech quality
  • speech perception
  • mean opinion scores
  • data mining
  • classification trees
  • regression

Authors’ Affiliations

Power, Acquisition and Telemetry Group, Schlumberger Technology Corporation, 150 Gillingham Lane, MD 1, Sugar Land, TX 77478, USA
Department of Electrical & Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada


© Zha and Chan 2005