Prosody-Based Automatic Segmentation of Speech into Sentences and Topics

A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. We investigate the use of prosody (information gleaned from the timing and melody of speech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Results show that the prosodic model alone performs on par with, or better than, word-based statistical language models -- for both true and automatically recognized words in news speech. The prosodic model achieves comparable performance with significantly less training data, and requires no hand-labeling of prosodic events. Across tasks and corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent boundary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pitch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2), Special Issue on Accessing Information in Spoken Audio, September 200

A Tool to Solve Sentence Segmentation Problem on Preparing Speech Database for Indonesian Text-to-speech System

AbstractCreating a training data ready to be used for developing a text-to-speech (TTS) system can be a difficult task, since sometimes the recorded audio data is not the same with the prepared texts. To overcome differences between audio and text data, we developed a tool to segment audio data into sentences. As it is known, doing sentence segmentation of audio data manually needs efforts and resources. This paper presents a solution for alleviating problems encountered during segmentation process of audio data for developing an Indonesian TTS system. The tool was developed based on a fact that bahasa Indonesia is a syllable-timed language. We found that our tool reduces resources needed for segmenting Indonesian audio data

Implementation Of Word Based Statistical Language Models

. In this paper we present an efficient data structure for storing trigram, bigram and unigram counts. The amount of memory required has been reduced by 53% compared to straightforward approaches. The average access time for retrieving information from the data structure has also slightly been reduced. Based upon this special data structure we have implemented several types of language models and applied them to the North American Business (NAB '94) recognition task. We show that both, the perplexity and the error rate could be reduced compared to the official NAB '94 trigram language model. 1 INTRODUCTION The main task of statistical language modelling is to provide a speech recognition system with the a-priori probabilities for a word sequence w 1 :::w N . In order to be able to compute the widely used bigram and trigram language models, we have to count how often a trigram or bigram, i.e. a word triple or a word pair, has been seen in a training corpus. We can then compute..