Open Access

Objective Speech Quality Measurement Using Statistical Data Mining

EURASIP Journal on Advances in Signal Processing20052005:721258

Received: 7 November 2003

Published: 21 June 2005


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 qualityspeech perceptionmean opinion scoresdata miningclassification treesregression

Authors’ Affiliations

Power, Acquisition and Telemetry Group, Schlumberger Technology Corporation, Sugar Land, USA
Department of Electrical & Computer Engineering, Queen's University, Kingston, Canada


© Zha and Chan 2005