PHAST: Spoken document retrieval based on sequence alignment
This paper presents a new approach to spoken document information retrieval for spontaneous speech corpora. Classical approach to this problem is the use of an automatic speech recognizer (ASR) combined with standard information retrieval techniques, based on terms or n-grams. However, state-of-the-...
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/86459 |
| Acceso en línea: | https://hdl.handle.net/2117/86459 |
| Access Level: | acceso abierto |
| Palabra clave: | Information retrieval Spoken document retrieval Approximate matching Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | This paper presents a new approach to spoken document information retrieval for spontaneous speech corpora. Classical approach to this problem is the use of an automatic speech recognizer (ASR) combined with standard information retrieval techniques, based on terms or n-grams. However, state-of-the-art large vocabulary continuous ASRs produce transcripts of spontaneous speech with a word error rate of 25% or higher, which is a drawback for retrieval techniques based on terms or n-grams. In order to overcome such a limitation, our method is based on a sequence alignment algorithm drawn from the field of bioinformatics to search |
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