Automatic Phoneme Segmentation Using Auditory Attention Features

Segmentation of speech into phonemes is beneficial for many spoken language processing applications. Here, a novel method which uses auditory attention features for detecting phoneme boundaries from acoustic signal is proposed. The auditory attention model can successfully detect salient audio events/sounds in an acoustic scene by capturing changes that make such salient events perceptually different than their neighbours. Therefore, it naturally offers an effective solution for segmentation task. The proposed phoneme segmentation method does not require transcription or acoustic models of phonemes. When evaluated on TIMIT, the proposed method is shown to successfully predict phoneme boundaries and outperform the recently published textindependent phoneme segmentation methods [1, 2].