Speech enhancementAutomatic speech recognition (ASR) is one of the most fascinating fields of research and the performance of ASR systems is most promising in a closed environment having negligible or zero background noise. However, the performance is not satisfactory if the spoken environment has a...
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Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation ro...
automatic speech recognition systemsnoise robustnessWe investigate the incorporation of frequency masking curves in the feature extraction module of automatic speech recognition systems to improve noise robustness. Frequency-masking curves are mathematically derived based on an auditory model, in which a ...
A Bayesian Approach to Deep Neural Network Adaptation with Applications to Robust Automatic Speech Recognition 基于贝叶斯的深度神经网络自适应及其在鲁棒自动语音识别中的应用 直接贝叶斯DNN自适应 使用高斯先验对DNN进行MAP自适应 为何贝叶斯在模型自适应中很有用?
A Novel Model Characteristics for Noise-Robust Automatic Speech Recognition Based on HMM This paper proposes a new model for a noise-robust Automatic Speech Recognition (ASR) based on parallel branch Hidden Markov Model (HMM) structure with a n... MS Rafieee,AA Khazaei - IEEE 被引量: 16发表...
In traditional methods for noise robust automatic speech recognition, the acoustic models are typically trained using clean speech or using multi-condition data that is processed by the same feature enhancement algorithm expected to be used in decoding. In this paper, we propose a noise adaptive tra...
Front-end techniques for robust automatic speech recognition (ASR) have been dominated by masking- and mapping-based deep learning approaches to speech enhancement. Previously, minimum mean-square error (MMSE) approaches to speech enhancement using Deep Xi (a deep learning approach to a priori SNR ...
Performance of an automatic speech recognition sys- tem degrades drastically when there is a mismatch be- tween training and testing conditions. The aim of ro- bust speech recognition is to overcome the mismatch problem so as to result in a moderate and grace- ful degradation in recognition per...
Speech recognitionMulti-task learningRobust ASRDenoising auto-encoderCHiME4In order to properly train an automatic speech recognition system, speech with its annotated transcriptions is required. The amount of realannotated data recorded in noisy and reverberant conditions......