Unicode-based graphemic systems for limited resource languages

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2015-August, Issue , 2015, Pages 5186-5190

  Gales, M J F ^a   Knill, K M ^a   Ragni, A ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

graphemic acoustic models; Low resource speech recognition

Indexed keywords

CONTINUOUS SPEECH RECOGNITION; DECISION TREES; LINGUISTICS; SPEECH COMMUNICATION; TELEPHONE SETS;

ACOUSTIC MODEL; CONTEXT DEPENDENT; DECISION TREE CONSTRUCTION; LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION; LOW RESOURCE LANGUAGES; LOW-RESOURCE SPEECH RECOGNITION; ROBUST ESTIMATION; SIMPLE APPROACH;

AUDIO SIGNAL PROCESSING;

EID: 84946055405     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2015.7178960     Document Type: Conference Paper

References (20)

1. S. Kanthak and H. Ney, "Context-dependent acoustic modelling using graphemes for large-vocabulary speech recognition," in Proc. ICASSP, 2002.
2. M. Killer, S. Stüker, and T. Schultz, "Grapheme based speech recognition," in Proc. EUROSPEECH, 2003.
3. S. Stüker and T. Schultz, "A Grapheme Based Speech Recognition System for Russian," in Proc. SPECOM'2004, 2004.
4. D. Vergyri et al., "Development of a conversational telephone speech recognizer for Levantine Arabic," in Proc. INTERSPEECH, 2005.
5. F. Diehl, M.J.F Gales, X. Liu, M. Tomalin, and P.C. Woodland, "Word boundary modelling and full covariance Gaussians for Arabic speech-to-text systems," in Proc. INTERSPEECH 2011, 2011, pp. 777-780.
6. V.-B. Le et al., "Developing STT and KWS systems using limited language resources," in Proc. INTERSPEECH 2014, 2014.
7. W. Basson and M. Davel, "Comparing grapheme-based and phoneme-based speech recognition for Afrikaans," in Proc. Pattern Recognition Association of South Africa (PRASA), 2012, http://www.prasa.org/proceedings/2012/prasa2012-29.pdf.
8. R. Molapo, E. Baranard, and F. de Wet, "Speech data collection in an under-resourced language within a multilingual context," in 4th International Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU), 2014.
9. V. B. Le and L. Besacier, "Comparison of Acoustic Modeling Techniques for Vietnamese and Khmer ASR," in Proc. ICSLP, 2006.
10. S. Sakti and S. Nakamura, "Recent progress in developing grapheme-based speech recognition for Indonesian ethnic languages: Javanese, Sundanese, Balinese and Bataks," in 4th International Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU), 2014.
11. R. Rasipuram, P. Bell, and M. Magimai-Doss, "Grapheme and multilingual posterior features for under-resourced speech recognition: a study on Scottish Gaelic," in Proc. ICASSP, 2013.
12. X. Anguera, J. Luque, and C. Gracia, "Audio-to-text alignment for speech recognition with very limited resources," in Proc. INTERSPEECH 2014, 2014.
13. "The unicode consortium," httlp://http://www. unicode.org, Accessed: 2014-09-30.
14. M. Harper, "IARPA Babel Program," http://www.iarpa.gov/Programs/ia/Babel/babel.html.
15. S. J. Young et al., The HTK Book (for HTK version 3.4.1), Cambridge University, 2009, http://htk.eng.cam.ac.uk.
16. D. Johnson et al., "QuickNet," http://www1.icsi.berkeley.edu/Speech/qn.html.
17. K.M. Knill, M.J.F. Gales, A. Ragni, and S. Rath, "Language Independent and Unsupervised Acoustic Models for Speech Recognition and Keyword Spotting," in Proc. INTERSPEECH 2014, 2014.
18. D. Povey et al., "The Kaldi Speech Recognition Toolkit," in IEEE 2011 Workshop on Automatic Speech Recognition and Understanding, 2011.
19. F. Diehl, M.J.F. Gales, M. Tomalin, and P.C.Woodland, "Morphological decomposition in Arabic ASR systems," Computer Speech &Language, vol. 26, no. 4, pp. 229-243, 2012.
20. M.J.F. Gales and F. Flego, "Model-based approaches for degraded channel modelling in robust ASR," in Proc. INTERSPEECH 2012, 2012.