Title: Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer
Authors: Švec, Jan
Lehečka, Jan
Šmídl, Luboš
Citation: ŠVEC, J. LEHEČKA, J. ŠMÍDL, L. Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. New York: International Speech Communication Association, 2022. s. 1886-1890. ISBN: neuvedeno , ISSN: 2308-457X
Issue Date: 2022
Publisher: International Speech Communication Association
Document type: konferenční příspěvek
URI: 2-s2.0-85140064214
ISBN: neuvedeno
ISSN: 2308-457X
Keywords in different language: Spoken Term Detection, Wav2Vec
Abstract in different language: 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.
Rights: Plný text není přístupný.
© 2022 ISCA
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