Non-intrusive Speech Quality Evaluation Research Articles

A Non-Intrusive speech quality evaluation algorithm for hearing aids via an auxiliary training task

With the increasing severity of hearing problems caused by an aging population, it has become more urgent to study speech quality evaluation methods for hearing aids to automatically judge the effect of parameter adjustments to solve the problem of lack of audiologists. A new evaluation algorithm, which is based on the multi-task learning strategy that combines the main task of non-intrusive speech quality evaluation with the auxiliary task of quality classification, is proposed. The main task network is composed of a bi-directional long short-term memory (BiLSTM) network combined with the self-attention mechanism, and the auxiliary task network is formed by fully connected layers based on batch normalization. The proposed algorithm firstly uses a convolutional neural network (CNN) to extract frame-level features. Then, the features are inputted into the main task network and the auxiliary task network to extract utterance-level features, respectively. Finally, the objective scores and quality ratings are obtained by the different mappers. Evaluation experiments of the Hearing Aid Speech Quality Index (HASQI) show that compared with others algorithms, the proposed algorithm effectively improves the speech quality evaluation. And it also shows strong robustness and good generalization performances under different distorted conditions.

A Non-Intrusive Speech Quality Evaluation Method Based on the Audiogram and Weighted Frequency Information for Hearing Aid

A Non-Intrusive Speech Quality Evaluation Method Based on the Audiogram and Weighted Frequency Information for Hearing Aid

Développer l’ACT face à la douleur chronique. L’efficience : un nouvel argument en faveur du changement ?

The human auditory system is modeled by different auditory models representing the distribution of speech sound energy in different channels across the cochlea using filter-banks of different bandwidths. In previous algorithms of non-intrusive speech quality evaluation, auditory features are determined using these auditory models on per frame basis and then averaged over the entire speech utterance. In these approaches, the effect of impulsive noise and other non-stationary noise effects get averaged over the utterance. To include the variations in the features of speech over time in the speech utterance, a multiple time-scale features approach has been proposed as the speech features vary from frame to frame that accounts for variation of noise characteristics over the speech utterance and thus its affect on quality mapping. In this work, non-intrusive speech quality evaluation has been done as an optimal linear combination of quality mapping called objective mean opinion score (MOS), computed using multiple time-scale estimates of features. The objective MOS of each of the multiple time-scale estimates (the combination of multiple active speeches) are obtained using a probabilistic approach. The overall objective MOS of the speech utterance is computed by taking the optimal linear combination of the estimated objective MOS using multiple time-scale estimates of features, where the optimality is based on the minimum mean square error (MMSE) criterion or correlation maximization criterion. The results are given in terms of Pearson's correlation coefficient and root mean square error (RMSE) between the subjective MOS and the estimated overall objective MOS for three different standard databases. The results have been compared with a single time-scale features approach, the ITU-T Recommendation P.563 and recent algorithms.

Non-intrusive speech quality estimation as combination of estimates using multiple time-scale auditory features

The human auditory system is modeled by different auditory models representing the distribution of speech sound energy in different channels across the cochlea using filter-banks of different bandwidths. In previous algorithms of non-intrusive speech quality evaluation, auditory features are determined using these auditory models on per frame basis and then averaged over the entire speech utterance. In these approaches, the effect of impulsive noise and other non-stationary noise effects get averaged over the utterance. To include the variations in the features of speech over time in the speech utterance, a multiple time-scale features approach has been proposed as the speech features vary from frame to frame that accounts for variation of noise characteristics over the speech utterance and thus its affect on quality mapping. In this work, non-intrusive speech quality evaluation has been done as an optimal linear combination of quality mapping called objective mean opinion score (MOS), computed using multiple time-scale estimates of features. The objective MOS of each of the multiple time-scale estimates (the combination of multiple active speeches) are obtained using a probabilistic approach. The overall objective MOS of the speech utterance is computed by taking the optimal linear combination of the estimated objective MOS using multiple time-scale estimates of features, where the optimality is based on the minimum mean square error (MMSE) criterion or correlation maximization criterion. The results are given in terms of Pearson's correlation coefficient and root mean square error (RMSE) between the subjective MOS and the estimated overall objective MOS for three different standard databases. The results have been compared with a single time-scale features approach, the ITU-T Recommendation P.563 and recent algorithms.

Non-intrusive speech quality assessment using several combinations of auditory features

Quality estimation of speech is essential for monitoring and maintenance of the quality of service at different nodes of modern telecommunication networks. It is also required in the selection of codecs in speech communication systems. There is no requirement of the original clean speech signal as a reference in non-intrusive speech quality evaluation, and thus it is of importance in evaluating the quality of speech at any node of the communication network. In this paper, non-intrusive speech quality assessment of narrowband speech is done by Gaussian Mixture Model (GMM) training using several combinations of auditory perception and speech production features, which include principal components of Lyon's auditory model features, MFCC, LSF and their first and second differences. Results are obtained and compared for several combinations of auditory features for three sets of databases. The results are also compared with ITU-T Recommendation P.563 for non-intrusive speech quality assessment. It is found that many combinations of these feature sets outperform the ITU-T P.563 Recommendation under the test conditions.

Non-Intrusive Speech Quality with Different Time Scale

Speech quality evaluation is an extremely important problem in modern communication networks. Service providers always strive to achieve a certain Quality of Service (QoS) in order to ensure customer satisfaction. Modeling the speech quality becomes an urgent issue. In this project a computable model for different time scale speech quality evaluation, called E-Model is developed. The results indicate that subjects can monitor speech quality variations very accurately with a delay of approximately 1 second. Non-intrusive speech quality is measured at the receiver from a degraded signal using G.107 (E-model) which is a parameter based model and calculate MOS values with quality rating factor. The quality rating factor is calculated by network impairments (loudness rating) of a speech. The output from the model described here is a scalar quality rating value, R, which varies directly with the overall conversational quality. The key contribution of this paper is to explore the use of G-107 (E-model) based features for different time scale non-intrusive speech quality evaluation using time varying loudness of a speech for long stimuli. Sectional speech quality is obtained by E- model, which is called instantaneous quality of the section that will be constant for each section. Overall perceived quality can be calculated by using average of instantaneous speech quality.

Two-scale Auditory Feature Based Non-intrusive Speech Quality Evaluation

This paper proposes a novel two-scale auditory feature based algorithm for non-intrusive evaluation of speech quality. The neuron firing probabilities along the length of the basilar membrane, from an explicit auditory model, are used to extract features from the distorted speech signal. This is in contrast to previous methods, which either use standard vocal tract based features, or incorporate only some aspects of the human auditory perception mechanism. The features are extracted at two scales, namely a global scale spanning all voiced frames in an utterance, and a local scale spanning voiced frames from contiguous voiced segments in the utterance. This is followed by a simple information fusion at the score level using Gaussian Mixture Models (GMMs). The use of an explicit auditory model to extract features is based on the premise that similar processing (in a qualitative sense) happens in human speech perception. In addition, auditory feature extraction at two scales incorporates the effects of both long term and short term distortions on speech quality. The proposed algorithm is shown to perform at least as good as the ITU-T Recommendation P.563.

New single-ended objective measure for non-intrusive speech quality evaluation

This article proposes a new output-based method for non-intrusive assessment of speech quality of voice communication systems and evaluates its performance. The method requires access to the processed (degraded) speech only, and is based on measuring perception-motivated objective auditory distances between the voiced parts of the output speech to appropriately matching references extracted from a pre-formulated codebook. The codebook is formed by optimally clustering a large number of parametric speech vectors extracted from a database of clean speech records. The auditory distances are then mapped into objective Mean Opinion listening quality scores. An efficient data-mining tool known as the self-organizing map (SOM) achieves the required clustering and mapping/reference matching processes. In order to obtain a perception-based, speaker-independent parametric representation of the speech, three domain transformation techniques have been investigated. The first technique is based on a perceptual linear prediction (PLP) model, the second utilises a bark spectrum (BS) analysis and the third utilises mel-frequency cepstrum coefficients (MFCC). Reported evaluation results show that the proposed method provides high correlation with subjective listening quality scores, yielding accuracy similar to that of the ITU-T P.563 while maintaining a relatively low computational complexity. Results also demonstrate that the method outperforms the PESQ in a number of distortion conditions, such as those of speech degraded by channel impairments.

Nonintrusive speech quality evaluation using an adaptive neurofuzzy inference system

This letter presents a novel nonintrusive speech quality evaluation method using an adaptive neurofuzzy inference system (ANFIS). The proposed method employed a first-order Sugeno-type fuzzy inference system (FIS) to estimate speech quality using only the output signal of the system under test. This new method was compared with the state-of-the-art nonintrusive quality evaluation standard, the ITU-T P.563 Recommendation, using seven subjective quality databases of the ITU-T P-series Supplementary 23. Experimental results show that the correlation of the proposed method with the subjective quality scores reached 0.8812, with a standard error of 0.3647 across the entire database. This compares favorably with the standard P.563, which provides a correlation and standard error of 0.8422 and 0.4493, respectively.