Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech

Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech Yang Liu ICSI and Purdue University yangl@icsi.berkeley.edu Andreas Stolcke Elizabeth Shriberg SRI and ICSI stolcke,ees@speech.sri.com Mary Harper Purdue University harper@ecn.purdue.edu Abstract We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and maximum likeli- hood estimation, and employs model interpolation to combine different representations of the data. The second approach models the posterior proba- bilities of the target classes; it is discriminative and integrates multiple knowledge sources in the max- imum entropy (maxent) framework. Both models combine lexical, syntactic, and prosodic informa- tion. We develop a technique for integrating pre- trained probability models into the maxent frame- work, and show that this approach can improve on an HMM-based state-of-the-art system for the sentence-boundary detection task. An even more substantial improvement is obtained by combining the posterior probabilities of the two systems. 1 Introduction Sentence boundary detection is a problem that has received limited attention in the text-based com- putational linguistics community (Schmid, 2000; Palmer and Hearst, 1994; Reynar and Ratnaparkhi, 1997), but which has recently acquired renewed im- portance through an effort by the DARPA EARS program (DARPA Information Processing Technol- ogy Office, 2003) to improve automatic speech tran- scription technology. Since standard speech recog- nizers output an unstructured stream of words, im- proving transcription means not only that word ac- curacy must be improved, but also that commonly used structural features such as sentence boundaries need to be recognized. The task is thus fundamen- tally based on both acoustic and textual (via auto- matic word recognition) information. From a com- putational linguistics point of view, sentence units are crucial and assumed in most of the further pro- cessing steps that one would want to apply to such output: tagging and parsing, information extraction, and summarization, among others. Sentence segmentation from speech is a difficult problem. The best systems benchmarked in a re- cent government-administered evaluation yield er- ror rates between 30% and 50%, depending on the genre of speech processed (measured as the num- ber of missed and inserted sentence boundaries as a percentage of true sentence boundaries). Because of the difficulty of the task, which leaves plenty of room for improvement, its relevance to real-world applications, and the range of potential knowledge sources to be modeled (acoustics and text-based, lower- and higher-level), this is an interesting chal- lenge problem for statistical and computational ap- proaches. All of the systems participating in the recent DARPA RT-03F Metadata Extraction evaluation (National Institute of Standards and Technology, 2003) were based on a hidden Markov model frame- work, in which word/tag sequences are modeled by N-gram language models (LMs). Additional fea- tures (mostly reflecting speech prosody) are mod- eled as observation likelihoods attached to the N- gram states of the HMM (Shriberg et al., 2000). The HMM is a generative modeling approach, since it describes a stochastic process with hidden variables …

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