Paper: Minimizing Word Error Rate In Textual Summaries Of Spoken Language

ACL ID A00-2025
Title Minimizing Word Error Rate In Textual Summaries Of Spoken Language
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2000
Authors

Automatic generation of text summaries for spoken language faces the problem of containing incorrect words and passages due to speech recognition er- rors. This paper describes comparative experiments where passages with higher speech recognizer confi- dence scores are favored in the ranking process. Re- sults show that a relative word error rate reduction of over 10% can be achieved while at the same time the accuracy of the summary improves markedly.