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Objective speech quality measurement using statistical data mining
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EURASIP Journal on Applied Signal Processing archiveVolume 2005 , Issue 1 (January 2005) table of contents
Pages: 1410 - 1424
Year of Publication: 2005
ISSN:1110-8657
Authors
Wei Zha
Power, Acquisition and Telemetry Group, Schlumberger Technology Corporation, Sugar Land, TX
Wai-Yip Chan
Department of Electrical & Computer Engineering, Queen's University, Kingston, ON, Canada
Publisher
Hindawi Publishing Corp. New York, NY, United States
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ABSTRACT
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.
REFERENCES
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INDEX TERMS
Primary Classification: I. Computing Methodologies I.2 ARTIFICIAL INTELLIGENCE I.2.7 Natural Language Processing Subjects: Speech recognition and synthesis
Additional Classification: H. Information Systems H.2 DATABASE MANAGEMENT H.2.8 Database applications Subjects: Data mining H.5 INFORMATION INTERFACES AND PRESENTATION (I.7) H.5.5 Sound and Music Computing Subjects: Signal analysis, synthesis, and processing I. Computing Methodologies I.5 PATTERN RECOGNITION I.5.1 Models Subjects: Statistical I.5.4 Applications Subjects: Signal processing
General Terms: Algorithms, Design, Measurement
Keywords: classification trees, data mining, mean opinion scores, regression, speech perception, speech quality
Collaborative Colleagues:
Wei Zha: colleagues
Wai-Yip Chan: colleagues