Related papers: Spoken Term Detection Methods for Sparse Transcription in Very Low-resource Settings

Spoken Term Detection Methods for Sparse Transcription in Very Low-resource Settings

URL: http://arxiv.org/abs/2106.06160v1
Date: Fri, 11 Jun 2021 04:09:54 GMT
Title: Spoken Term Detection Methods for Sparse Transcription in Very Low-resource Settings
Authors: \'Eric Le Ferrand, Steven Bird, Laurent Besacier
Abstract summary: Experiments on two oral languages show that a pretrained universal phone recognizer, fine-tuned with only a few minutes of target language speech, can be used for spoken term detection. We show that representing phoneme recognition ambiguity in a graph structure can further boost the recall while maintaining high precision in the low resource spoken term detection task.
Score: 20.410074074340447
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We investigate the efficiency of two very different spoken term detection approaches for transcription when the available data is insufficient to train a robust ASR system. This work is grounded in very low-resource language documentation scenario where only few minutes of recording have been transcribed for a given language so far.Experiments on two oral languages show that a pretrained universal phone recognizer, fine-tuned with only a few minutes of target language speech, can be used for spoken term detection with a better overall performance than a dynamic time warping approach. In addition, we show that representing phoneme recognition ambiguity in a graph structure can further boost the recall while maintaining high precision in the low resource spoken term detection task.

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