CNN-based spoken term detection and localization without dynamic programming

Tzeviya Sylvia Fuchs, Yael Segal, Joseph Keshet

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

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 languageEnglish
Pages (from-to)6853-6857
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume2021-June
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE

Keywords

  • Convolutional neural networks
  • Event detection
  • Speech processing
  • Spoken term detection

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