A class-specific speech enhancement for phoneme recognition: a dictionary learning approach

Nazreen, P M and Ramakrishnan, A G and Ghosh, Prasanta Kumar (2017) A class-specific speech enhancement for phoneme recognition: a dictionary learning approach. In: 17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016), SEP 08-12, 2016, San Francisco, CA, pp. 3728-3732.

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Abstract

We study the influence of using class-specific dictionaries for enhancement over class-independent dictionary in phoneme recognition of noisy speech. We hypothesize that, using class specific dictionaries would remove the noise more compared to a class-independent dictionary, thereby resulting in better phoneme recognition. Experiments are performed with speech data from TIMIT corpus and noise samples from NOISEX-92 database. Using KSVD, four types of dictionaries have been learned: class-independent, manner-of-articulation-class, place-of-articulation-class and 39 phoneme-class. Initially, a set of labels are obtained by recognizing the speech, enhanced using a class-independent dictionary. Using these approximate labels, the corresponding class-specific dictionaries are used to enhance each frame of the original noisy speech, and this enhanced speech is then recognized. Compared to the results obtained using the class-independent dictionary, the 39 phoneme class based dictionaries provide a relative phoneme recognition accuracy improvement of 5.5%, 3.7%, 2.4% and 2.2%, respectively for factory2, m109, leopard and babble noises, when averaged over 0, 5 and 10 dB SNRs.

Item Type:	Conference Paper
Series.:	Interspeech
Additional Information:	Copy right for this article belongs to the ISCA-INT SPEECH COMMUNICATION ASSOC, C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	30 Oct 2017 03:39
Last Modified:	30 Oct 2017 03:39
URI:	http://eprints.iisc.ac.in/id/eprint/58128

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