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Detection of acoustic-phonetic landmarks in mismatched conditions using a biomimetic model of human auditory processing
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In: http://www.isle.uiuc.edu/%7Esborys/king_coling12.pdf (2012)
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Detection of acoustic-phonetic landmarks in mismatched conditions using a biomimetic model of human auditory processing
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In: http://aclweb.org/anthology/C/C12/C12-2058.pdf (2012)
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Abstract:
Acoustic-phonetic landmarks provide robust cues for speech recognition and are relatively invariant between speakers, speaking styles, noise conditions and sampling rates. The ability to detect acoustic-phonetic landmarks as a front-end for speech recognition has been shown to improve recognition accuracy. Biomimetic inter-spike intervals and average signal level have been shown to accurately convey information about acoustic-phonetic landmarks. This paper explores the use of inter-spike interval and average signal level as input features for landmark detectors trained and tested on mismatched conditions. These detectors are designed to serve as a front-end for speech recognition systems. Results indicate that landmark detectors trained using inter-spike intervals and signal level are relatively robust to both additive channel noise and changes in sampling rate.
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Keyword:
Acoustic-Phonetic Landmark; Auditory Modeling; Mismatched Conditions. 589
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URL: http://aclweb.org/anthology/C/C12/C12-2058.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.379.1980
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Detection of acoustic-phonetic landmarks in mismatched conditions using a biomimetic model of human auditory processing
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In: http://www.ifp.illinois.edu/speech/pubs/2012/king12coling.pdf (2012)
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Pipelined back-propagation for context-dependent deep neural networks
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In: http://mi.eng.cam.ac.uk/~xc257/papers/Pipelined_DNN.pdf (2012)
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On Developing An Automatic Speech Recognition System For Standard Arabic Language ...
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On Developing An Automatic Speech Recognition System For Standard Arabic Language ...
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Phoneme Class Based Adaptation for Mismatch Acoustic Modeling of Distant Noisy Speech (Preprint)
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In: DTIC (2012)
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Semi-supervised learning for acoustic and prosodic modeling in speech applications
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