Analysis/synthesis of speech using the short-time Fourier transform and a time-varying ARMA process

Andreas Spanias, Philipos Loizou, Gim Lim, Ye Chen, Gen Hu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


A speech analysis/synthesis system that relies on a time-varying Auto Regressive Moving Average (ARMA) process and the Short-Time Fourier Transform (STFT) is proposed. The narrowband components in speech are represented in the frequency domain by a set of harmonic components, while the broadband random components are represented by a time-varying ARMA process. The time-varying ARMA model has a dual function, namely, it creates a spectral envelope that fits accurately the harmonic STFT components, and provides for the spectral representation of the broadband components of speech. The proposed model essentially combines the features of waveform coders by employing the STFT and the features of traditional vocoders by incorporating an appropriately shaped noise sequence.

Original languageEnglish (US)
Pages (from-to)645-652
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number4
StatePublished - Apr 1993

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics


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