We describe an algorithm for monitoring subjective speech quality without access to the original signal that has very low computational and memory requirements. The features used in the proposed algorithm can be computed from commonly used speechcoding parameters. Reconstruction and perceptual transformation of the signal are not performed. The algorithm generates quality assessment ratings without explicit distortion modeling. The simulation results indicate that the proposed non-intrusive objective quality measure performs better than the ITU-T P.563 standard despite its very low computational complexity. Index Terms: non-intrusive quality assessment, quality of service. 1.

Monitoring of speech quality in emerging heterogeneous networks is of great interest to network operators. The most efficient way to satisfy such a need is through nonintrusive, objective speech quality assessment. In this paper, we describe a low-complexity algorithm for monitoring the speech quality over a network. The features used in the proposed algorithm can be computed from commonly used speech-coding parameters. Reconstruction and perceptual transformation of the signal is not performed. The critical advantage of the approach lies in generating quality assessment ratings without explicit distortion modeling. The results from the performed experiments indicate that the proposed nonintrusive objective quality measure performs better than the ITU-T P.563 standard

A speech coder tailored especially for the frame-erasure channel-the sinusoidal voice over packet coder (SVOPC)-is proposed. Based on a classified approach, avoiding interframe coding techniques, and synthesizing its output from slowly varying parameters, the coder is inherently robust to packet loss. SVOPC is based on quasi-harmonic modeling of the linear prediction (LP) residual. Both the sinusoidal amplitudes and phases are explicitly encoded using new methods based on Gaussian mixture models. A wide-band (16-kHz sampling frequency) implementation of the coder provides synthesized speech of good subjective quality at around 20 kbps. SVOPC is evaluated by means of subjective listening tests, and compared to a reference system based on G.722.2 (the AMR wide-band codec). Under frame erasure conditions (5%-30% frame erasures generated according to a Gilbert model), SVOPC clearly outperforms G.722.2.

In this paper, Gaussian mixture (GM) models are used to design variable-dimension quantizers according to a weighted distortion criterion. A general method for combining a variable-to-fixed dimension transform, with GM modeling and quantization, is proposed. The method provides a convenient and efficient way to encode the amplitudes in a sinusoidal speech coder. Quantizers designed according to the proposed scheme are evaluated both according to weighted distortion criteria, and with respect to a high-rate bound approximation of the distortion. Informal listening tests suggest that the amplitudes can be encoded without subjective loss in a wideband harmonic coder, at a rate around 40 bits per frame (for the amplitudes only).

We propose an error protection and packet loss concealment system, based on a modified sinusoidal vocoder. By means of a few additional phase parameters, the vocoder is able to partially track the original signal during voiced segments. This signal tracking allows us to switch seamlessly between the vocoded signal and a primary encoded signal. A packetized speech transmission system, using PCM data as primary encoding, and the modified vocoder as a sub-coder, is simulated and evaluated using subjective preference testing. Packet loss concealment based on extrapolation of the vocoder parameters is found to perform well, especially at high error rates.

Lately, Gaussian mixture (GM) models have found new applications in speech processing, and particularly in speech coding. This paper provides a review of GM based quantization and prediction. The main contribution is a discussion on GM model optimization. Two previously presented algorithms of EM-type are analyzed in some detail, and models are estimated and evaluated experimentally using theoretical measures as well as GM based speech spectrum coding and prediction. It has been argued that since many sources have a bounded support, this should be utilized in both the choice of model, and the optimization algorithm. By low-dimensional modeling examples, illustrating the behavior of the two algorithms graphically, and by full-scale evaluation of GM based systems, the advantages of a bounded support approach are quantified. For all evaluation techniques in the study, model accuracy is improved when the bounded support approach is adopted. The gains are typically largest for models with diagonal covariance matrices.

In this paper, a packet loss concealment scheme based on sinusoidal extrapolation is proposed. It is to be implemented at the receiver end of a packetized speech transmission system and conceals frame-erasures in a PCM waveform. The scheme operates on the source-filter components of the speech signal, which are obtained through Linear Prediction (LP) analysis. When one or more speech frames are missing, the LP residual and filter coefficients are extrapolated separately. The residual is extrapolated based on sinusoidal modeling of the last correct residual frame, whereas the latest filter is repeated and bandwidth expanded. The proposed scheme is compared to two benchmark systems by means of subjective testing. The benchmark systems are based on repetitions of the last pitch period; one operates on the source-filter components, the other directly on the speech signal. It is noted that the systems operating on the source-filter components are preferred by the listeners, and that the proposed system is clearly superior to both benchmark systems.