Subword-based approaches for spoken document retrieval
Speech Communication
Inference of Variable-length Acoustic Units for Continuous Speech Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Probabilistic methods for searching ocr-degraded arabic text
Probabilistic methods for searching ocr-degraded arabic text
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Rapid resource transfer for multilingual natural language processing
Rapid resource transfer for multilingual natural language processing
Error correction vs. query garbling for Arabic OCR document retrieval
ACM Transactions on Information Systems (TOIS)
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Combining LVCSR and vocabulary-independent ranked utterance retrieval for robust speech search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Spoken Content Retrieval: A Survey of Techniques and Technologies
Foundations and Trends in Information Retrieval
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This paper introduces a new approach to ranking speech utterances by a system's confidence that they contain a spoken word. Multiple alternate pronunciations, or degradations, of a query word's phoneme sequence are hypothesized and incorporated into the ranking function. We consider two methods for hypothesizing these degradations, the best of which is constructed using factored phrase-based statistical machine translation. We show that this approach is able to significantly improve upon a state-of-the-art baseline technique in an evaluation on held-out speech. We evaluate our systems using three different methods for indexing the speech utterances (using phoneme, phoneme multigram, and word recognition), and find that degradation modeling shows particular promise for locating out-of-vocabulary words when the underlying indexing system is constructed with standard word-based speech recognition.