---
res:
  bibo_abstract:
  - In this paper we present a speech presence probability (SPP) estimation algorithmwhich
    exploits both temporal and spectral correlations of speech. To this end, the SPP
    estimation is formulated as the posterior probability estimation of the states
    of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm
    to decode the 2D-HMM which is based on the turbo principle. The experimental results
    show that indeed the SPP estimates improve from iteration to iteration, and further
    clearly outperform another state-of-the-art SPP estimation algorithm.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Dang Hai Tran
      foaf_name: Vu, Dang Hai Tran
      foaf_surname: Vu
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Haeb-Umbach, Reinhold
      foaf_surname: Haeb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  bibo_doi: 10.1109/ICASSP.2013.6637771
  dct_date: 2013^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1520-6149
  dct_language: eng
  dct_subject:
  - correlation methods
  - estimation theory
  - hidden Markov models
  - iterative methods
  - probability
  - spectral analysis
  - speech processing
  - 2D HMM
  - SPP estimates
  - iterative algorithm
  - posterior probability estimation
  - spectral correlation
  - speech presence probability estimation
  - state-of-the-art SPP estimation algorithm
  - temporal correlation
  - turbo principle
  - two-dimensional hidden Markov model
  - Correlation
  - Decoding
  - Estimation
  - Iterative decoding
  - Noise
  - Speech
  - Vectors
  dct_title: Using the turbo principle for exploiting temporal and spectral correlations
    in speech presence probability estimation@
...