Single-sided speech quality measurement

Abstract

A non-intrusive speech quality estimation technique is based on statistical or probability models such as Gaussian Mixture Models (“GMMs”). Perceptual features are extracted from the received speech signal and assessed by an artificial reference model formed using statistical models. The models characterize the statistical behavior of speech features. Consistency measures between the input speech features and the models are calculated to form indicators of speech quality. The consistency values are mapped to a speech quality score using a mapping optimized using machine learning algorithms, such as Multivariate Adaptive Regression Splines (“MARS”). The technique provides competitive or better quality estimates relative to known techniques while having lower computational complexity.

Description

FIELD OF THE INVENTION [0001] This invention relates generally to the field of telecommunications, and more particularly to double-ended measurement of speech quality. BACKGROUND OF THE INVENTION [0002] The capability of measuring speech quality in a telecommunications network is important to telecommunications service providers. Measurements of speech quality can be employed to assist with network maintenance and troubleshooting, and can also be used to evaluate new technologies, protocols and equipment. However, anticipating how people will perceive speech quality can be difficult. The traditional technique for measuring speech quality is a subjective listening test. In a subjective listening test a group of people manually, i.e., by listening, score the quality of speech according to, e.g., an Absolute Categorical Rating (“ACR”) scale, Bad (1), Poor (2), Fair (3), Good (4), Excellent (5). The average of the scores, known as a Mean Opinion Score (“MOS”), is then calculated and use...

AI Technical Summary

Benefits of technology

[0007] One advantage of the inventive technique is reduction of processing requirements for speech quality measurement without significant degradation in performance. Simulations with Perceptual Linear Prediction (“PLP”) coefficients have shown that the inventive technique can outperform P.563 by up to 44.74% in correlation R for SMV coded speech under noisy conditions. The inventive technique is comparable to P.563 under various other conditions. An average 40% reduction in processing time was obtained compared to P.563, with P.563 implemented using a quicker procedural computer language than the interpretive language used to run the inventive technique. Thus, the speedup that can be obtained from the inventive technique programmed with a procedural language such as C is expected to be much greater.

Problems solved by technology

However, anticipating how people will perceive speech quality can be difficult.

Regardless of the scoring scheme, subjective listening tests are time consuming and costly.

In a working commercial network it may be difficult to provide both the clean signal and the received speech signal to the test equipment because of the distances between endpoints.

However, each of these known non-intrusive measurement schemes is computationally intensive relative to the capabilities of equipment which could currently be widely deployed at low cost.

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