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 used to characterize the performance of speech codecs, transmission equipment, and networks. Other kinds of subjective tests and scoring schemes may also be used, e.g., degradation mean opinion scores (“DMOS”). Regardless of the scoring scheme, subjective listening tests are time consuming and costly.
Machine-automated, “objective” measurement is known as an alternative to subjective listening tests. Objective measurement provides a rapid and economical means to estimate user opinion, and makes it possible to perform real-time speech quality measurement on a network-wide scale. Objective measurement can be performed either intrusively or non-intrusively. Intrusive measurement, also called double-ended or input-output-based measurement, is based on measuring the distortion between the received and transmitted speech signals, often with an underlying requirement that the transmitted signal be a “clean” signal of high quality. Non-intrusive measurement, also called single-ended or output-based measurement, does not require the clean signal to estimate quality. 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. Consequently, non-intrusive techniques should be more practical for implementation outside of a test facility because they do not require a clean signal.
Several non-intrusive measurement schemes are known. In C. Jin and R. Kubichek. “Vector quantization techniques for output-based objective speech quality,” in Proc. IEEE Inf. Conf Acoustics, Speech, Signal Processing, vol. 1, May 1996, pp. 491-494, comparisons between features of the received speech signal and vector quantizer (“VQ”) codebook representations of the features of clean speech are used to estimate quality. In W. Li and R. Kubichek, “Output-based objective speech quality measurement using continuous hidden Markov models,” in Proc. 7th Inl. Strap. Signal Processing Applications, vol. I. July 2003. pp. 389-392, the VQ codebook reference is replaced with a hidden Markov model. In P. Gray, M. P. Hollier. and R. E. Massara. “Non-intrusive speech-quality assessment using vocal-tract models,” Proc. Inst. Elect. Eng., Vision, Image. Signal Process., vol. 147, no. 6, pp. 493-501, December 2000 and D. S. Kim. “ANIQUE: An auditory model for single-ended speech quality estimation,” IEEE Trans. Speech Audio Process., vol. 13, no. 5, pp. 821-831, September 2005, vocal tract modeling and modulation-spectral features derived from the temporal envelope of speech, respectively, provide quality cues for non-intrusive quality measurement. More recently, a non-intrusive method using neurofuzzy inference was proposed in G. Chen and V. Parsa, “Nonintrusive speech quality evaluation using an adaptive neurofuzzy inference system,” IEEE Signal Process. Lett., vol. 12, no. 5, pp. 403-106, May 2005. The International Telecommunications Union ITU-T P.563 standard represents the “state-of-the-art” algorithm, ITU-T P.563, Single Ended Method for Objective Speech Quality Assessment in Narrow-Band Telephony Applications, International Telecommunication Union, Geneva, Switzerland, May 2004. 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. Consequently, a less computationally intensive non-intrusive solution would be desirable in order to facilitate deployment outside of test facilities.