Conference paper

SINISCALCHI Sabato M., SCHWARZ Petr and LEE Chin-Hui. High-accuracy phone recognition by combining high performance lattice generation and knowledge based rescoring. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007). Hononulu: IEEE Signal Processing Society, 2007, pp. 869-872. ISBN 1-4244-0728-1.
Publication language:english
Original title:High-accuracy phone recognition by combining high performance lattice generation and knowledge based rescoring
Title (cs):Rozpoznávání fónů s velkou přesností pomocí kombinace vysoce kvalitního generování svazů a reskórování založenného na znalostech
Pages:869-872
Proceedings:Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)
Conference:32nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Place:Hononulu, US
Year:2007
ISBN:1-4244-0728-1
Publisher:IEEE Signal Processing Society
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2007/siniscalchi_icassp_2007-chl.pdf [PDF]
Keywords
phone recognition
Annotation
The paper is about high-accuracy phone recognition by combining high performance lattice generation and knowledge based rescoring
Abstract
This study is a result of a collaboration project between two groups, one from Brno University of Technology and the other from Georgia Institute of Technology (GT). Recently the Brno recognizer is known to outperform many state-of-the-art systems on phone recognition, while the GT knowledge-based lattice rescoring module has been shown to improve system performance on a number of speech recognition tasks. We believe a combination of the two system results in high-accuracy phone recognition. To integrate the two very different modules, we modify Brno's phone recognizer into a phone lattice hypothesizer to produce high-quality phone lattices, and feed them directly into the knowledge-based module to rescore the lattices. We test the combined system on the TIMIT continuous phone recognition task without retraining the individual subsystems, and we observe that the phone error rate was effectively reduced to 19.78% from 24.41% produced by the Brno phone recognizer. To the best of the authors' knowledge this result represents the lowest ever error rate reported on the TIMIT continuous phone recognition task.
BibTeX:
@INPROCEEDINGS{
   author = {M. Sabato Siniscalchi and Petr Schwarz and Chin-Hui Lee},
   title = {High-accuracy phone recognition by combining high
	performance lattice generation and knowledge based rescoring},
   pages = {869--872},
   booktitle = {Proc. IEEE International Conference on Acoustics, Speech and
	Signal Processing (ICASSP 2007)},
   year = {2007},
   location = {Hononulu, US},
   publisher = {IEEE Signal Processing Society},
   ISBN = {1-4244-0728-1},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=8462}
}

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