Abstract
Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural networks. Our approach consists in analyzing the error profile of a model trained to predict speech features frame-by-frame. Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error. We evaluate our system on the TIMIT dataset, with improvements over similar methods.
Original language | English |
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Title of host publication | Proceedings of the Student Research Workshop at the Annual Meeting of the Association for Computational Linguistics |
Pages | 62-68 |
Number of pages | 7 |
ISBN (Electronic) | 978-1-945626-56-2 |
DOIs | |
Publication status | Published - 2017 |
MoE publication type | A4 Article in a conference publication |
Event | Annual Meeting of the Association for Computational Linguistics: Student Research Workshop - Vancouver, Canada Duration: 30 Jul 2017 → 4 Aug 2017 |
Conference
Conference | Annual Meeting of the Association for Computational Linguistics |
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Abbreviated title | SRW |
Country | Canada |
City | Vancouver |
Period | 30/07/2017 → 04/08/2017 |