Variability Tolerant Audio Motif Discovery
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
A Computational Model of Language Acquisition: the Emergence of Words
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Modelling early language acquisition skills: towards a general statistical learning mechanism
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Summarizing multiple spoken documents: finding evidence from untranscribed audio
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
NLP on spoken documents without ASR
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
From audio recurrences to TV program structuring
AIEMPro '11 Proceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services
ACM Transactions on Information Systems (TOIS)
A Computational Model of Language Acquisition: the Emergence of Words
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Bootstrapping a unified model of lexical and phonetic acquisition
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Joint training of non-negative Tucker decomposition and discrete density hidden Markov models
Computer Speech and Language
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We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a prespecified inventory of lexical units (i.e., phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multiword phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.