Generalized likelihood ratio test for voiced-unvoiced decision in noisy speech using the harmonic model

Etan Fisher, Joseph Tabrikian, Shlomo Dubnov

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

In this paper, a novel method for voiced-unvoiced decision within a pitch tracking algorithm is presented. Voiced-unvoiced decision is required for many applications, including modeling for analysis/synthesis, detection of model changes for segmentation purposes and signal characterization for indexing and recognition applications. The proposed method is based on the generalized likelihood ratio test (GLRT) and assumes colored Gaussian noise with unknown covariance. Under voiced hypothesis, a harmonic plus noise model is assumed. The derived method is combined with a maximum a-posteriori probability (MAP) scheme to obtain a pitch and voicing tracking algorithm. The performance of the proposed method is tested using several speech databases for different levels of additive noise and phone speech conditions. Results show that the GLRT is robust to speaker and environmental conditions and performs better than existing algorithms.

Original languageEnglish
Pages (from-to)502-510
Number of pages9
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume14
Issue number2
DOIs
StatePublished - 1 Dec 2006

Keywords

  • Generalized likelihood ratio test (GLRT)
  • Harmonic model
  • Likelihood ratio test (LRT)
  • Maximum a-posteriori probability
  • Noisy speech
  • Pitch tracking
  • Voice activity detection (VAD)
  • Voiced-unvoiced decision

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Generalized likelihood ratio test for voiced-unvoiced decision in noisy speech using the harmonic model'. Together they form a unique fingerprint.

Cite this