Spontaneous Speech Effects In Large Vocabulary Speech Recognition Applications

12871

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nan

Abstract

We describe three analyses on the effects of spontaneous speech on continuous speech recognition performance. We have found that: (1) spontaneous speech effects signifi- cantly degrade recognition performance, (2) fluent sponta- neous speech yields word accuracies equivalent to read speech, and (3) using spontaneous speech training data can significantly improve performance for recognizing sponta- neous speech. We conclude that word accuracy can be improved by explicitly modeling spontaneous effects in the recognizer, and by using as much spontaneous speech train- ing data as possible. Inclusion of read speech training data, even within the task domain, does not significantly improve performance. 1.

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Full Content

Spontaneous Speech Effects In Large Vocabulary Speech Recognition Applications John Butzberger, Hy Murveit, Elizabeth Shriberg, Patti Price SRI International Speech Research and Technology Program Menlo Park, CA 94025 ABSTRACT We describe three analyses on the effects of spontaneous speech on continuous speech recognition performance. We have found that: (1) spontaneous speech effects signifi- cantly degrade recognition performance, (2) fluent sponta- neous speech yields word accuracies equivalent to read speech, and (3) using spontaneous speech training data can significantly improve performance for recognizing sponta- neous speech. We conclude that word accuracy can be improved by explicitly modeling spontaneous effects in the recognizer, and by using as much spontaneous speech train- ing data as possible. Inclusion of read speech training data, even within the task domain, does not significantly improve performance. 1. INTRODUCTION Recognition of spontaneous speech is an important feature of database-query spoken-language systems (SLS). How- ever, most speech recognition research has focussed on acoustic and language modeling developed for recognition of read speech [1]. Read speech has been used extensively in the past for both training and testing speech recognition systems because it is significantly less expensive to collect than spontaneous speech, and because the lexical and syn- tactic content of the data can be controlled. The multi-site data collection effort [3] has provided a chal- lenging corpus for research and development in the Airline Travel Information System (ATIS) domain. We have observed a significant increase in word error rate compared to the previous task domain, the read-speech naval Resource Management (RM) task [2,6]. Word error rates for RM systems have typically been in the 5% range, whereas ATIS word error rates have exceeded 10% [4], for comparable perplexities. The speaking style typically exhibited in the RM domain had a very consistent rate and articulation, within and across sentences, and across speakers. There were no dis- fluencies, such as word fragments, hesitations, or self-edits, since utterances containing these effects were removed from the corpus. The utterances tended to be short and direct (3.3 seconds long, on average). No pause fillers (uh, um), false starts, repairs, or excessively long pauses occurred. The speakers were able to concentrate on speech production, rather than query formation or problem solv- ing. Furthermore, the training and testing texts were gener- ated using a fixed vocabulary, and with the same, known language model, which quite adequately represented the source and target languages. The speaking style typically exhibited in the ATIS domain differs from that in the RM domain all of the above aspects. The speaking rate is highly inconsistent, both within utter- ances, across utterances within a session, and across ses- sions and speakers. The articulation is highly variable, with stressed forms of function words and reduced forms of con- tent words typically not observed in read speech. The sen- tence lengths vary widely, and are typically longer than RM sentences (7.5 seconds long, on average). Some words in ATIS sentences may not exist in the recognizer's lexicon, and an appropriate language model must be developed. …

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Labeled Text

We describe three analyses on the effects of spontaneous speech on continuous speech recognition performance. We have found that: (1) spontaneous speech effects signifi- cantly degrade recognition performance, (2) fluent sponta- neous speech yields word accuracies equivalent to read speech, and (3) using spontaneous speech training data can significantly improve performance for recognizing sponta- neous speech. We conclude that word accuracy can be improved by explicitly modeling spontaneous effects in the recognizer, and by using as much spontaneous speech train- ing data as possible. Inclusion of read speech training data, even within the task domain, does not significantly improve performance. 1.

Publication Details

Paper ID:
12871
Year:
1992
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan