A deep neural network approach for sentence boundary detection in broadcast news
Chenglin Xu, Lei Xie, Guangpu Huang, Xiong Xiao, Eng Siong Chng, Haizhou Li · 2014
This paper presents a deep neural network (DNN) approach to sentence boundary detection in broadcast news. We extract prosodic and lexical features at each inter-word position in the transcripts and learn a sequential classifier to label these po-sitions as either boundary or non-boundary. This work is real-ized by a hybrid DNN-CRF (conditional random field) architec-ture. The DNN accepts prosodic feature inputs and non-linearly maps them into boundary/non-boundary posterior probability outputs. Subsequently, the posterior probabilities are combined with lexical features and the integrated features are modeled by a linear-chain CRF. The CRF finally labels the inter-word po-sitions as boundary or non-boundary by Viterbi decoding. Ex-periments show that, as compared with the state-of-the-art DT-CRF approach [1], the proposed DNN-CRF approach achieves 16.7 % and 4.1 % reduction in NIST boundary detection error in reference and speech recognition transcripts, respectively. Index Terms: sentence boundary detection, structural event de-tection, deep neural network, rich transcription 1.