This paper proposes an efficient method for estimating frame energy of speech from EnhancedVariable Rate Coder (EVRC) bitstream for network-based speech processing applications in Transcoder Free Operation (TrFO) environments, where speech signals are represented as speech coding parameters. A frame of speech energy is decomposed into the energy of excitation and vocal tract filter, and the frame energy estimation method is derived for each component. Among many parameters of EVRC bitstream, the fixed codebook gain and adaptive codebook gain are used for the estimation of excitation energy, and Line Spectrum Pair (LSP) information is used to estimate the energy of vocal tract filter. Experimental results demonstrated the novelty of the proposed method. The correlation coefficient between the actual and estimated frame energy can be maintained at a value of 0.994 with just 5% multiplicative operations of full decoding.

In this paper, we propose a novel model for estimating the quality of speech without the reference speech information. The proposed auditory non-intrusive quality estimation plus (ANIQUE+) model is a perceptual model simulating the functional role of human auditory system, and employs improved modeling of quality estimation by statistical learning methods. Experimental evaluation demonstrated that the performance of the ANIQUE+ model is significantly superior to that of the current ITU-T standard recommendation P.563 on 34 different subjective mean opinion score (MOS) databases - the averaged correlation between subjective and objective quality scores is about 0.97 for ANIQUE+, whereas P.563 shows 0.87 averaged correlation.

In order to estimate the quality of degraded speech processed by communication networks, conventional objective speech quality assessment methods require the source speech signal, which has been applied to the networks, as well as the processed speech. The paper presents a new paradigm in objective speech quality assessment. In contrast to previous objective models, the proposed auditory non-intrusive quality estimation (ANIQUE) model estimates the quality of speech without using the source speech information. ANIQUE is a perceptual model which simulates the functional role of the human auditory system at both peripheral level and central auditory level. The performance of ANIQUE is demonstrated using 19 different subjective MOS databases.

In this paper, we present a novel neural network-based predictor for subjective quality of speech signals. The output from the predictor is the estimated subjective quality or mean opinion score (MOS). The internal representation of signals is calculated using a model for the human auditory system. The perceptual distance between the reference speech and the speech sample under test is used as input to the neural network, which is then trained to model the underlying relationship between this perceptual distance and its subjective quality (MOS). Accurate MOS predictions have been demonstrated for speech coders used in common wireless applications including AMPS, TDMA, GSM and CDMA. MOS values predicted by the neural network MOS machine (NN-MM) were validated for clean and corrupted channels as well as for background noise conditions. Prediction accuracy is an order of magnitude better than anything previously reported, with worst case errors on the order of 0.05 MOS point.

ATM has been recommended by the CCITT as the transport vehicle for the future B-ISDN networks. In ATM-based networks, a set of user declared parameters that describes the traffic characteristics, is required for the connection acceptance control (CAC) and traffic enforcement (policing) mechanisms. At the call set-up phase, the CAC algorithm uses those parameters to make a call acceptance decision. During the call progress, the policing mechanism uses the same parameters to control the user's traffic within its declared values in order to protect the network's resources and avoid possible congestion problems. A novel policing mechanism using neural networks (NNs) is presented. This is based upon an accurate estimation of the probability density function (pdf) of the traffic via its count process and implemented using NNs. The pdf-based policing is made possible only by NNs because pdf policing requires complex calculations, in real-time, at very high speeds. The architecture of the policing mechanism is composed of two interconnected NNs. The first one is trained to learn the pdf of "ideal nonviolating" traffic, whereas the second is trained to capture the "actual" characteristics of the "actual" offered traffic during the progress of the call. The output of both NNs is compared. Consequently, an error signal is generated whenever the pdf of the offered traffic violates its "ideal" one. The error signal is then used to shape the traffic back to its original values.>

Accurate characterization of the multimedia traffic is essential, in asynchronous transfer mode (ATM) broadband networks, in order to develop a robust set of traffic descriptors. Such a set is required, by the usage parameter control (UPC) algorithm, for traffic enforcement (policing). The authors present a novel approach to characterize and model the multimedia traffic using neural networks (NNs). A backpropagation NN is used to characterize and predict the statistical variations of the packet arrival process resulting from the superposition of N packetized video sources and M packetized voice sources. The accuracy of the results is verified by matching the index of dispersion for counts (IDC), the variance, and the autocorrelation of the arrival process to those of the NN output. The results show that the NNs can be successfully utilized to characterize the complex nonrenewal process with extreme accuracy.>