Blind phoneme segmentation with temporal prediction errors
This addresses a critical step in speech recognition systems, but it is incremental as it builds on existing unsupervised methods.
The paper tackles unsupervised phoneme segmentation in speech by analyzing prediction errors from sequence models like Markov chains and RNNs, achieving improvements over similar methods on the TIMIT dataset.
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.