Single-sided speech quality measurement

a speech quality and single-sided technology, applied in the field of single-sided speech quality measurement, can solve the problems of difficult to provide both the clean signal and the received speech signal, time-consuming and costly subjective listening tests, and difficulty in anticipating how people will perceive speech quality, so as to reduce processing time, reduce processing requirements, and reduce the effect of significant performance degradation

Inactive Publication Date: 2007-08-30
AVAYA INC
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  • Abstract
  • Description
  • Claims
  • Application Information

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 co...

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 rec...

Method used

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  • Single-sided speech quality measurement
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Examples

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Embodiment Construction

[0009]FIG. 1 illustrates a relatively easily calculable non-intrusive measurement technique. The input is a speech (“test”) signal for which a subjective quality score is to be estimated (100), e.g., a speech signal that has been processed by network equipment, transmitted on a communications link, or both. A feature extraction module (102) is employed to extract perceptual features, frame by frame, from the test signal. A time segmentation module (104) labels the feature vector of each frame as belonging to one of three possible segment classes: voiced, unvoiced, or inactive. In a separate process, statistical or probability models such as Gaussian Mixture Models are formed. The terms “statistical model” and “statistical reference model” as used herein encompass probability models, statistical probability models and the like, as those terms are understood in the art. Different models may be formed for different classes of speech signals. For instance, one class could be high-qualit...

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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...

Claims

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Application Information

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IPC IPC(8): G10L19/00
CPCG10L25/69
Inventor CHAN, WAI-YIPFALK, TIAGO H.EL-HENNAWEY, MOHAMED
Owner AVAYA INC
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