A method for open-vocabulary speech-driven text retrieval

Authors:
Atsushi Fujii;Katunobu Itou;Tetsuya Ishikawa
Affiliations:
University of Library and Information Science, Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan;University of Library and Information Science, Tsukuba, Japan
Venue:
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Year:
2002

Citing 6
Cited 1

Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
New techniques for open-vocabulary spoken document retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving two-stage ad-hoc retrieval for short queries

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Spoken document representations for probabilistic retrieval

Speech Communication - Special issue on accessing information in spoken audio
Speech-Driven Text Retrieval: Using Target IR Collections for Statistical Language Model Adaptation in Speech Recognition

Information Retrieval Techniques for Speech Applications [this book is based on the workshop “Information Retrieval Techniques for Speech Applications”, held as part of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in New Orleans, USA, in September 2001].
Speech-based retrieval using semantic co-occurrence filtering

HLT '94 Proceedings of the workshop on Human Language Technology

VideoQA: question answering on news video

MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia

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Abstract

While recent retrieval techniques do not limit the number of index terms, out-of-vocabulary (OOV) words are crucial in speech recognition. Aiming at retrieving information with spoken queries, we fill the gap between speech recognition and text retrieval in terms of the vocabulary size. Given a spoken query, we generate a transcription and detect OOV words through speech recognition. We then correspond detected OOV words to terms indexed in a target collection to complete the transcription, and search the collection for documents relevant to the completed transcription. We show the effectiveness of our method by way of experiments.