Blind Phoneme Segmentation With Temporal Prediction Errors
Paul Michel, Okko Johannes Räsänen, Roland Thiollière, Emmanuel Dupoux · 2017
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 frameby-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.