• DocumentCode
    178416
  • Title

    Parametric representation for singing voice synthesis: A comparative evaluation

  • Author

    Babacan, Onur ; Drugman, Thomas ; Raitio, Tuomo ; Erro, Daniel ; Dutoit, Thierry

  • Author_Institution
    TCTS Lab., Univ. of Mons, Mons, Belgium
  • fYear
    2014
  • fDate
    4-9 May 2014
  • Firstpage
    2564
  • Lastpage
    2568
  • Abstract
    Various parametric representations have been proposed to model the speech signal. While the performance of such vocoders is well-known in the context of speech processing, their extrapolation to singing voice synthesis might not be straightforward. The goal of this paper is twofold. First, a comparative subjective evaluation is performed across four existing techniques suitable for statistical parametric synthesis: traditional pulse vocoder, Deterministic plus Stochastic Model, Harmonic plus Noise Model and GlottHMM. The behavior of these techniques as a function of the singer type (baritone, counter-tenor and soprano) is studied. Secondly, the artifacts occurring in high-pitched voices are discussed and possible approaches to overcome them are suggested.
  • Keywords
    hidden Markov models; speech synthesis; statistical analysis; vocoders; GlottHMM; baritone; counter-tenor; deterministic plus stochastic model; extrapolation; harmonic plus noise model; high-pitched voices; parametric representations; singer type; singing voice synthesis; soprano; speech processing; speech signal model; statistical parametric synthesis; traditional pulse vocoder; vocoders; Frequency modulation; Harmonic analysis; Hidden Markov models; Speech; Speech synthesis; Stochastic processes; Vocoders; Parametric Representation; Singing Voice; Synthesis; Vocoder;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
  • Conference_Location
    Florence
  • Type

    conf

  • DOI
    10.1109/ICASSP.2014.6854063
  • Filename
    6854063