Sentence unit detection using lexical information for automatic speech recognition transcripts - Ho, Thi Nga (2019)
This thesis studies Sentence Unit Detection (SUD) that uses lexical information for Automatic Speech Recognition (ASR) transcripts. An SUD system detects the presence or absence of a sentence unit boundary at every word boundaries of an unpunctuated text document. A transcript produced by an ASR system from a given speech audio input is an unpunctuated text document. Hence, SUD for ASR transcripts will increase the readability and enable the usability of ASR transcripts in downstream text processing applications, such as machine translation and option mining, which require sentence unit boundary information in their input. As usually being a post-processing application for ASR transcripts, an SUD system may benefit from two major information sources, i.e., the prosodic information obtained from the audio input and the lexical information obtained from the ASR transcripts, for its model learning. In this thesis, we chose to use only lexical information because it has shown to be essential for SUD research as compared to the other. Moreover, using lexical information takes an advantage of the massive available lexical data sources from the Internet. Additionally, SUD models that use only lexical information are usually much less complex as compared to the models that employ multiple information sources.