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Enhancing ASR Systems for Under-Resourced Languages through a Novel Unsupervised Acoustic Model Training TechniqueCUCU, H. , BUZO, A. , BESACIER, L. , BURILEANU, C.
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speech recognition, under-resourced languages, unsupervised acoustic modeling, unsupervised training
speech(15), training(13), unsupervised(12), resourced(5), recognition(5), processing(5), languages(5), language(5), acoustic(5), system(4)
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About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 63 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01009
Web of Science Accession Number: 000352158600009
SCOPUS ID: 84924787729
Statistical speech and language processing techniques, requiring large amounts of training data, are currently state-of-the-art in automatic speech recognition. For high-resourced, international languages this data is widely available, while for under-resourced languages the lack of data poses serious problems. Unsupervised acoustic modeling can offer a cost and time effective way of creating a solid acoustic model for any under-resourced language. This study describes a novel unsupervised acoustic model training method and evaluates it on speech data in an under-resourced language: Romanian. The key novel factor of the method is the usage of two complementary seed ASR systems to produce high quality transcriptions, with a Character Error Rate (ChER) < 5%, for initially untranscribed speech data. The methodology leads to a relative Word Error Rate (WER) improvement of more than 10% when 100 hours of untranscribed speech are used.
|References|||||Cited By «-- Click to see who has cited this paper|
| L. Besacier, E. Barnard, A. Karpov, T. Schultz, "Automatic speech recognition for under-resourced languages: A survey.", in Speech Communication, Vol. 56 - Special Issue on Processing Under-Resourced Languages, pp. 85-100, |
[CrossRef] [Web of Science Times Cited 98] [SCOPUS Times Cited 135]
 H. Cucu, "Towards a speaker-independent, large-vocabulary continuous speech recognition system for Romanian", PhD Thesis, University "Politehnica" of Bucharest, 2011.
 A. Buzo, H. Cucu, C. Burileanu, "Text Spotting In Large Speech Databases For Under-Resourced Languages", in Proc. Int. Conf. Speech Technology and Human-Computer Dialogue (SpeD), Cluj-Napoca, Romania, 2013, pp. 77-82,
[CrossRef] [SCOPUS Times Cited 4]
 H. Cucu, A. Buzo, C. Burileanu, "Unsupervised Acoustic Model Training using Multiple Seed ASR Systems", in Proc. Int. Workshop on Spoken Language Technologies for Under-resourced Languages (SLTU), St. Petersburg, Russia, 2014, pp. 124-130.
 G. Zavaliagkos, T. Colthurst, "Utilizing Untranscribed Training Data to Improve Performance", in DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, USA, 1998, pp. 301-305
 T. Kemp and A. Waibel, "Unsupervised Training of a Speech Recognizer: Recent Experiments", in Proc. Eurospeech, Budapest, Hungary, 1999, pp. 2725-2728.
 F. Wessel and H. Ney, "Unsupervised training of acoustic models for large vocabulary continuous speech recognition", in Proc. Automatic Speech Recognition and Understanding Workshop (ASRU), Trento, Italy, 2001, pp. 307-310,
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 62]
 L. Lamel, J.-L. Gauvain, G. Adda, "Lightly Supervised and Unsupervised Acoustic Model Training", in Computer Speech & Language, vol. 16, pp. 115-129, 2002. Available:
[CrossRef] [Web of Science Times Cited 133] [SCOPUS Times Cited 168]
 T. Fraga-Silva, J.-L. Gauvain, L. Lamel, "Lattice-based Unsupervised Acoustic Model Training", in Proc. Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011, pp. 4656-4659,
[CrossRef] [SCOPUS Times Cited 17]
 L. Wang, M. J. F. Gales and P. C. Woodland, "Unsupervised training for mandarin broadcast news and conversational transcription", in Proc. Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Honolulu, Hawaii, 2007, vol. IV, pp. 353-356,
[CrossRef] [SCOPUS Times Cited 26]
 J. Ma, S. Matsoukas., "Unsupervised training on a large amount of Arabic news broadcast data", in Proc. Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Hawaii, 2007, vol. II, pp. 349-352,
[CrossRef] [SCOPUS Times Cited 10]
 K. Yu, M. J. F. Gales, L. Wang and P. C. Woodland, "Unsupervised training and directed manual transcription for LVCSR", in Speech Communication, Vol. 52, pp. 652-663, 2010. Available:
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 41]
 J. Loof, C. Gollan, and H. Ney, "Cross-language Bootstrapping for Unsupervised Acoustic Model Training: Rapid Development of a Polish Speech Recognition System", in Proc. INTERSPEECH, Brighton, U.K., 2009, pp. 88-91.
 N. T. Vu, F. Kraus and T. Schultz, "Cross-language bootstrapping based on completely unsupervised training using multilingual A-stabil", in Proc. Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011, pp. 5000-5003,
[CrossRef] [SCOPUS Times Cited 29]
 N. T. Vu, F. Kraus and T. Schultz, "Rapid building of an ASR system for Under-Resourced Languages based on Multilingual Unsupervised Training", In Proc. INTERSPEECH, Florence, Italy, 2011, pp. 3145-3148.
 N. T. Vu, F. Kraus and T. Schultz, "Multilingual A-stabil: A new confidence score for multilingual unsupervised training", in Spoken Language Technology Workshop (SLT), Berkeley, California, USA, 2010, pp. 183-188,
[CrossRef] [SCOPUS Times Cited 21]
 H. Cucu, A. Buzo, L. Petrica, D. Burileanu and C. Burileanu, "Recent Improvements of the SpeeD Romanian LVCSR System", in Proc. Int. Conf. on Communications (COMM), Bucharest, Romania, 2014, pp. 111-114,
[CrossRef] [SCOPUS Times Cited 12]
 CMU Sphinx Toolkit: [Online] Available: Temporary on-line reference link removed - see the PDF document
 SRI-LM Toolkit: [Online] Available: Temporary on-line reference link removed - see the PDF document
 M. Rouvier, G. Dupuy, P. Gay, E. Khoury, T. Merlin, S. Meignier, "An Open-source State-of-the-art Toolbox for Broadcast News Diarization," in Proc. INTERSPEECH, Lyon, France, 2013.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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