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We present a non-intrusive codec type and bit-rate detection algorithm that extracts a number of features from a decoded speech signal and models their statistics using a Deep Neural Network (DNN) classifier. We also present a method for reducing the computational complexity and improving the robustness of the algorithm by pruning features that have a low importance and high computational cost using...
We present a phoneme confusion analysis that models the impact of reverberation on automatic speech recognition performance by formulating the problem in a Bayesian framework. Our analysis under reverberant conditions shows the relative robustness to reverberation of each phoneme and also indicates that substitutions and deletions correspond to the most common errors in a phoneme recognition task...
We present NISQ, a data-driven non-intrusive speech quality measure that has been trained to predict the PESQ score for a given speech signal. NISQ is based on feature extraction and a binary tree regression based model. A training method using the intrusive PESQ algorithm to automatically label large quantities of speech data is presented and utilized. Our method is shown to predict PESQ with an...
We present a non-intrusive data driven method for codec detection and identification in the presence of background noise. The method uses a number of speech features which are then used to train a CART classifier. We demonstrate the performance of the method using several different noise types over a wide range of SNRs. Our results show that we can identify a codec and its bit rate to an accuracy...
This paper addresses the performance of objective methods for speech quality assessment in signals with realistic, block-varying degradations. A block algorithm is presented which employs an existing data-driven approach and is shown to outperform current standard algorithms. We present test results performed on a block-varying extension to the C-Qual database. The effects of block size on the accuracy...
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