Simultaneous recognition of words and prosody in the Boston University Radio Speech Corpus

Mark Hasegawa-Johnson, Ken Chen, Jennifer Cole, Sarah Borys, Sung Suk Kim, Aaron Cohen, Tong Zhang, Jeung Yoon Choi, Heejin Kim, Taejin Yoon, Sandra Chavarria

Research output: Contribution to journalArticlepeer-review


This paper describes automatic speech recognition systems that satisfy two technological objectives. First, we seek to improve the automatic labeling of prosody, in order to aid future research in automatic speech understanding. Second, we seek to apply statistical speech recognition models of prosody for the purpose of reducing the word error rate of an automatic speech recognizer. The systems described in this paper are variants of a core dynamic Bayesian network model, in which the key hidden variables are the word, the prosodic tag sequence, and the prosody-dependent allophones. Statistical models of the interaction among words and prosodic tags are trained using the Boston University Radio Speech Corpus, a database annotated using the tones and break indices (ToBI) prosodic annotation system. This paper presents both theoretical and empirical results in support of the conclusion that a prosody-dependent speech recognizer-a recognizer that simultaneously computes the most-probable word labels and prosodic tags-can provide lower word recognition error rates than a standard prosody-independent speech recognizer in a multi-speaker speaker-dependent speech recognition task on radio speech.

Original languageEnglish (US)
Pages (from-to)418-439
Number of pages22
JournalSpeech Communication
Issue number3-4
StatePublished - Jul 2005


  • Automatic speech recognition
  • Prosody

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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