Acoustic segmentation using switching state Kalman filter

Research output: Contribution to journalConference articlepeer-review


Segmenting the acoustic signal in the TIMIT database by a switching state Kalman filter model is reported in this paper. According to the assumption that the high dimensional acoustic feature vector of the LSF (Line Spectrum Frequency) of the speech signal is probably embedded in a low dimensional space, a two dimensional vector is used to represent the continuous state vector in this model. The parameters of the model are initialized by PPCA (probabilistic principle component analysis) and first order vector auto-regression, and are re-estimated by the EM algorithm. We show that this model can be used to classify vowels, nasals, frication and silence by an approximate Viterbi inference.

Original languageEnglish (US)
Pages (from-to)752-755
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2003
Event2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong
Duration: Apr 6 2003Apr 10 2003

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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