Diagnostic Prediction of Transmitted Speech Quality: A New Framework for Signal-based and Parametric Models
نویسندگان
چکیده
In this paper, we present a new framework for the diagnostic prediction of transmitted speech quality. The idea is to extract perceptually relevant feature estimations from the speech signal, and combine them with an overall quality metric in order to obtain more reliable as well as more diagnostic predictions of speech quality. We implement this framework in two complementary ways: In terms of a signal-based model which can be used for online and offline measurement, and in terms of a parametric model which can be used for network planning. The implementations are compared to standard state-of-the-art models and show a similar level of reliability, while providing additional diagnostic value.
منابع مشابه
Speech enhancement based on hidden Markov model using sparse code shrinkage
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 ...
متن کاملUtilizing Kernel Adaptive Filters for Speech Enhancement within the ALE Framework
Performance of the linear models, widely used within the framework of adaptive line enhancement (ALE), deteriorates dramatically in the presence of non-Gaussian noises. On the other hand, adaptive implementation of nonlinear models, e.g. the Volterra filters, suffers from the severe problems of large number of parameters and slow convergence. Nonetheless, kernel methods are emerging solutions t...
متن کاملSignal Prediction by Layered Feed - Forward Neural Network (RESEARCH NOTE).
In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...
متن کاملA New Method for Speech Enhancement Based on Incoherent Model Learning in Wavelet Transform Domain
Quality of speech signal significantly reduces in the presence of environmental noise signals and leads to the imperfect performance of hearing aid devices, automatic speech recognition systems, and mobile phones. In this paper, the single channel speech enhancement of the corrupted signals by the additive noise signals is considered. A dictionary-based algorithm is proposed to train the speech...
متن کاملUsing Context-based Statistical Models to Promote the Quality of Voice Conversion Systems
This article aims to examine methods of optimizing GMM-based voice conversion systems performance in which GMM method is introduced as the basic method for improvement of voice conversion systems performance. In the current methods, due to using a single conversion function to convert all speech units and subsequent spectral smoothing arising from statistical averaging, we will observe quality ...
متن کامل