Unsupervised Extraction of Recurring Words from Infant-Directed Speech

Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Volumn , Issue , 2011, Pages 2006-2011

  McInnes, Fergus R ^a   Goldwater, Sharon J ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

computational modelling; language acquisition; speech recognition; word segmentation

Indexed keywords

CLUSTERING ALGORITHMS; COMPUTATIONAL LINGUISTICS; EXTRACTION; INFORMATION THEORY; MODELING LANGUAGES;

COMPUTATIONAL MODELLING; INCREMENTAL METHOD; INFANT-DIRECTED SPEECH; LANGUAGE ACQUISITION; LEARN+; NOVEL INFORMATION; SIMILAR PATTERN; SINGLE PASS; UNSUPERVISED EXTRACTION; WORD SEGMENTATION;

SPEECH RECOGNITION;

EID: 85139468758     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Aimetti, G. (2009). Modelling early language acquisition skills: Towards a general statistical learning mechanism. In Proceedings of the student research workshop at EACL.
2. Bortfeld, H., Morgan, J., Golinkoff, R., & Rathbun, K. (2005). Mommy and me: Familiar names help launch babies into speech stream segmentation. Psychological Science, 16(4), 298–304.
3. Brent, M. (1999). An efficient, probabilistically sound algorithm for segmentation and word discovery. Machine Learning, 34, 71–105.
4. Brent, M., & Siskind, J. (2001). The role of exposure to isolated words in early vocabulary development. Cognition, 81(2), B33–44.
5. Christiansen, M., Allen, J., & Seidenberg, M. (1998). Learning to segment speech using multiple cues: A connectionist model. Language and Cognitive Processes, 13, 221–268.
6. Driesen, J., ten Bosch, L., & Van hamme, H. (2009). Adaptive non-negative matrix factorization in a computational model of language acquisition. In Proceedings of interspeech.
7. Goldwater, S., Griffiths, T., & Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112(1), 21–54.
8. Jansen, A., Church, K., & Hermansky, H. (2010). Towards spoken term discovery at scale with zero resources. In Proceedings of Interspeech (pp. 1676–1679).
9. Jusczyk, P., Houston, D., & Newsome, M. (1999). The beginnings of word segmentation in English-learning infants. Cognitive Psychology, 39, 159–207.
10. MacWhinney, B., & Snow, C. (1985). The child language data exchange system. Journal of Child Language, 12, 271–296.
11. Myers, C. S., Rabiner, L. R., & Rosenberg, A. E. (1980). Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28, 623–635.
12. Neubig, G., Mimura, M., Mori, S., & Kawahara, T. (2010). Learning a language model from continuous speech. In Proceedings of Interspeech (pp. 1053–1056).
13. Newman, M. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69(066133).
14. Park, A. S., & Glass, J. R. (2008). Unsupervised pattern discovery in speech. IEEE Transactions on Audio, Speech and Language Processing, 16, 186–197.
15. Räsänen, O. (2010). Fully unsupervised word learning from continuous speech using transitional probabilities of atomic acoustic events. In Proceedings of interspeech.
16. Rytting, A. (2007). Preserving subsegmental variation in modeling word segmentation (or, the raising of Baby Mondegreen). Unpublished doctoral dissertation.
17. Saffran, J., Newport, E., & Aslin, R. (1996). Word segmentation: the role of distributional cues. Journal of Memory and Language, 35, 606–621.
18. Thiessen, E., & Saffran, J. (2003). When cues collide: Use of stress and statistical cues to word boundaries by 7- to 9-month-old infants. Developmental Psychology, 39(4), 706–716.