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 factor-ization. The signal model of the observation as a linear combination of sample spectrograms is augmented by a mel-spectral feature domain convolution to account for the effects of room(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)
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)
Introduction • Watkins (2005) has shown that listeners use information about the preceding context of a reverberated test word to help them identify it. • This suggests a mechanism of perceptual constancy that confers robustness in reverberant environments. • Watkins' experiments focused on one particular speech identification task ('sir' or 'stir'), and(More)
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