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Blind phoneme segmentation with temporal prediction errors

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arxiv 1608.00508 v2 pith:ODMLFWAX submitted 2016-08-01 cs.CL

Paul Michel , Okko Räsänen , Roland Thiollière , Emmanuel Dupoux This is my paper
classification cs.CL
keywords speechpredictionerrorsegmentationalgorithmanalyzingapproachblind
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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 network. 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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Representing Speech Through Autoregressive Prediction of Cochlear Tokens

    cs.CL 2025-08 conditional novelty 6.0 of 10

    Autoregressive prediction over discrete cochlear tokens yields a speech representation that beats prior self-supervised models on lexical-semantic similarity and is competitive on SUPERB tasks.

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