Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43965704" target="_blank" >RIV/49777513:23520/22:43965704 - isvavai.cz</a>
Result on the web
<a href="https://www.isca-speech.org/archive/interspeech_2022/svec22_interspeech.html" target="_blank" >https://www.isca-speech.org/archive/interspeech_2022/svec22_interspeech.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21437/Interspeech.2022-10409" target="_blank" >10.21437/Interspeech.2022-10409</a>

Result language
angličtina
Original language name
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer
Original language description
In recent years, the standard hybrid DNN-HMM speech recognizers are outperformed by the end-to-end speech recognition systems. One of the very promising approaches is the grapheme Wav2Vec 2.0 model, which uses the self-supervised pretraining approach combined with transfer learning of the fine-tuned speech recognizer. Since it lacks the pronunciation vocabulary and language model, the approach is suitable for tasks where obtaining such models is not easy or almost impossible. In this paper, we use the Wav2Vec speech recognizer in the task of spoken term detection over a large set of spoken documents. The method employs a deep LSTM network which maps the recognized hypothesis and the searched term into a shared pronunciation embedding space in which the term occurrences and the assigned scores are easily computed. The paper describes a bootstrapping approach that allows the transfer of the knowledge contained in traditional pronunciation vocabulary of DNN-HMM hybrid ASR into the context of grapheme-based Wav2Vec. The proposed method outperforms the previously published system based on the combination of the DNN-HMM hybrid ASR and phoneme recognizer by a large margin on the MALACH data in both English and Czech languages.
Czech name
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Czech description
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Project
<a href="/en/project/VJ01010108" target="_blank" >VJ01010108: Robust processing of recordings for operations and security</a><br>
Continuities
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Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
ISBN
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ISSN
2308-457X
e-ISSN
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Number of pages
5
Pages from-to
1886-1890
Publisher name
International Speech Communication Association
Place of publication
New York
Event location
Incheon, Korea
Event date
Sep 18, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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