Language-resource independent speech segmentation using cues from a spectrogram image

In this paper, we use image processing techniques on the speech spectrogram to perform speech phoneme segmentation. The proposed method relies solely on visual cues on the spectrogram, without the need for language-specific training data. The results are evaluated on the TIMIT corpus, and compared to other unsupervised speech segmentation techniques, with comparable results obtained. We also fuse the results with those obtained by hidden Markov models (HMM) and HMM-based forced alignment to investigate if image features can provide an additional feature representation for speech processing tasks. With the fusion, up to 10% absolute improvement in segmentation accuracy over the HMM baselines can be obtained. Results are promising and suggests a strong potential for image-based features applying to speech processing.

1. Introduction

Speech parameters currently being used are mostly spectral features, such as mel frequency cepstral coefficients (MFCC) and perceptual linear predictive (PLP) coefficients. These features are proven to be effective as a form of speech representation as they are derived from modeling of the human vocal tract.