Abstract
In this paper, we propose a spoken term detection algorithm for simultaneous prediction and localization of in-vocabulary and out-of-vocabulary terms within an audio segment. The proposed algorithm infers whether a term was uttered within a given speech signal or not by predicting the word embeddings of various parts of the speech signal and comparing them to the word embedding of the desired term. The algorithm utilizes an existing embedding space for this task and does not need to train a task-specific embedding space. At inference the algorithm simultaneously predicts all possible locations of the target term and does not need dynamic programming for optimal search. We evaluate our system on several spoken term detection tasks on read speech corpora.
Original language | English |
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Pages (from-to) | 6853-6857 |
Number of pages | 5 |
Journal | Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing |
Volume | 2021-June |
DOIs | |
State | Published - 2021 |
Event | 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021 |
Bibliographical note
Publisher Copyright:© 2021 IEEE
Funding
T. S. Fuchs is sponsored by the Malag scholarship for outstanding doctoral students in the high tech professions. Y. Segal is sponsored by the Ministry of Science & Technology, Israel. The authors would like to thank Sharadhi Alape Suryanarayana for helpful comments on this paper.
Funders | Funder number |
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Ministry of science and technology, Israel |
Keywords
- Convolutional neural networks
- Event detection
- Speech processing
- Spoken term detection