Recognition Of Reverberant Speech Using Frequency Domain Linear Prediction
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This paper presents a novel approach to predicting the intrusiveness of background noises in speech signals as it is perceived by human listeners. This problem is of particular interest in telephony, where the recently widened range of transmitted audio fr ...
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The development of an Automatic Speech Recognition (ASR) system for the bilingual MediaParl corpus is challenging for several reasons: (1) reverberant recordings, (2) accented speech, and (3) no prior information about the language. In that context, we emp ...
ISCA2014
This thesis deals with signal-based methods that predict how listeners perceive speech quality in telecommunications. Such tools, called objective quality measures, are of great interest in the telecommunications industry to evaluate how new or deployed sy ...
EPFL2016
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Is it possible to predict the intrusiveness of background noise in speech signals as perceived by humans? Such a question is important to the automatic evaluation of speech enhancement systems, including those designed for new wideband speech telephony, an ...
Idiap2014
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The development of an Automatic Speech Recognition (ASR) system for the bilingual MediaParl corpus is challenging for several reasons: (1) reverberant recordings, (2) accented speech, and (3) no prior information about the language. In that context, we emp ...
Idiap2014
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The log-energy parameter, typically derived from a full-band spectrum, is a critical feature commonly used in automatic speech recognition (ASR) systems. However, log-energy is difficult to estimate reliably in the presence of background noise. In this pap ...
