Browsing by Author "Chan, Wai-Yip"

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    Single-sided speech quality measurement
    (Avaya Inc, 2007) Chan, Wai-Yip; Falk, Tiago; El-Hennawey, Mohamed
    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.
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    Speech quality measurement based on classification estimation
    (RPX Clearinghouse LLC, 2006) Chan, Wai-Yip; Zha, Wei; El-Hennawey, Mohamed
    Auditory processing is used in conjunction with cognitive mapping to produce an objective measurement of speech quality that approximates a subjective measurement such as MOS. In order to generate a data model for measuring speech quality from a clean speech signal and a degraded speech signal, the clean speech signal is subjected to auditory processing to produce a subband decomposition of the clean speech signal; the degraded speech signal is subjected to auditory processing to produce a subband decomposition of the degraded speech signal; and cognitive mapping is performed based on the clean speech signal, the subband decomposition of the clean speech signal, and the subband decomposition of the degraded speech signal. Various statistical analysis techniques, such as MARS and CART, may be employed, either alone or in combination, to perform data mining for cognitive mapping. From the large number of features extracted from the distortion surface, MARS is employed to find a smaller subset of features to form the speech quality estimator. The subset of feature variables, together with the particular manner of combining them, are jointly optimized to produce a statistically consistent estimate (data model) of subjective opinion scores such as MOS.