Amy V. Beeston

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The problem of reverberation in speech recognition is addressed in this study by extending a noise-robust feature enhancement method based on non-negative matrix factorization. The signal model of the observation as a linear combination of sample spectrograms is augmented by a melspectral feature domain convolution to account for the effects of room(More)
Mounting evidence suggests that listeners perceptually compensate for the adverse effects of reverberation in rooms when listening to speech monaurally. However, it is not clear whether the underlying perceptual mechanism would be at all effective in the high levels of stimulus uncertainty that are present in everyday listening. Three experiments(More)
Listeners were asked to identify modified recordings of the words "sir" and "stir," which were spoken by an adult male British-English speaker. Steps along a continuum between the words were obtained by a pointwise interpolation of their temporal-envelopes. These test words were embedded in a longer "context" utterance, and played with different amounts of(More)
This paper presents a novel system for automatic assessment of pronunciation quality of English learner speech, based on deep neural network (DNN) features and phoneme specific discriminative classifiers. DNNs trained on a large corpus of native and non-native learner speech are used to extract phoneme posterior probabilities. A part of the corpus includes(More)
Human listeners are able to perceptually compensate for the effects of reverberation on speech recognition, by exploiting information gleaned from prior exposure to the reverberant environment. We present a computer model of perceptual compensation for reverberation implemented within a hidden Markov model speech recogniser, in which different reverberant(More)
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