AlxwaciThis contribution introduces instrumental speech quality measures for the GSM system based only on transmission parameters. Certain combinations of CSM parameters which quantify the transmission quality in terms of bit error rate, received power level, etc., were shown be suitable for the prediction of the resulting speech quality. Neither the original nor the received speech signal is n...

The E-model brings a modern approach to the computation of estimated quality, allowing for easy implementation. One of its advantages is that it can be applied in real time. The method is based on a mathematical computation model evaluating transmission path impairments influencing speech signal, especially delays and packet losses. These parameters, common in an IP network, can affect speech q...

Vector Quantization (VQ) has been extensively used in speech vocoders. The training process normally requires a very large training-set. This paper introduces a novel energy distribution comparison distortion measure for the high-band speech spectrum that enables the vector quantizer to operate given a relatively small training-set. This measure has been used in the construction of a segmental ...

In this paper, the authors propose the Beta-Order Minimum Mean-Square Error (MMSE) Spectral Amplitude estimator with Chi statistical models for the speech priors. The new estimator incorporates both a shape parameter on the distribution and cost function parameter. The performance of the MMSE Beta-Order Spectral Amplitude estimator with Chi speech prior is evaluated using the Segmental Signal-t...

In recent years a great deal of effort has been expended to develop methods that determine speech quality through the use of comparative algorithms. These methods are designed to calculate an index value of quality that correlates to a mean opinion score given by human subjects in evaluation sessions. In this paper, we validate Perceptual Evaluation of Speech Quality (PESQ) ITU-T Recommendation...

This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...

Speech enhancement employing deep neural networks (DNNs) for denoising is called noise suppression (DNS). The DNS trained with mean squared error (MSE) losses cannot guarantee good perceptual quality. Perceptual evaluation of speech quality (PESQ) a widely used metric evaluating However, the original PESQ algorithm non-differentiable, therefore, directly be as optimization criterion gradient-ba...

In this paper we describe a double-ended (i.e. reference-based or intrusive) approach to objective quality estimation of synthetic speech that uses a linear regression model whose parameters can easily be interpreted. The model was trained and evaluated on English data from the 2008 to 2013 Blizzard Challenges (BC) [1], which is the largest publically available resource of listener-evaluated sy...