Edit disfluency detection and correction using a cleanup language model and an alignment model

Jui Feng Yeh, Chung-Hsien Wu

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

This investigation presents a novel approach to detecting and correcting the edit disfluency in spontaneous speech. Hypothesis testing using acoustic features is first adopted to detect potential interruption points (IPs) in the input speech. The word order of the cleanup utterance is then cleaned up based on the potential IPs using a class-based cleanup language model, the dclctablc region and the correction are aligned using an alignment model. Finally, log linear weighting is applied to optimize the performance. Using the acoustic features, the IP detection rate is significantly improved especially in recall rate. Based on the positions of the potential IPs, the cleanup language model and the alignment model are able to detect and correct the edit disflucncy efficiently. Experimental results demonstrate that the proposed approach has achieved error rates of 0.33 and 0.21 for IP detection and edit word deletion, respectively.

Original languageEnglish
Pages (from-to)1574-1583
Number of pages10
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume14
Issue number5
DOIs
Publication statusPublished - 2006 Sep 1

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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