Multi-view features in a DNN-CRF model for improved sentence unit detection on English broadcast news

This paper presents a deep neural network-conditional random field (DNN-CRF) system with multi-view features for sentence unit detection on English broadcast news. We proposed a set of multi-view features extracted from the acoustic, articulatory, and linguistic domains, and used them together in the DNN-CRF model to predict the sentence boundaries. We tested the accuracy of the multi-view features on the standard NIST RT-04 English broadcast news speech data. Experiments show that the best system outperforms the state-of-the-art sentence unit detection system significantly by 13.2% absolute NIST sentence error rate reduction using the reference transcription. However, the performance gain is limited on the recognized transcription partly due to the high word error rate.