• DocumentCode
    3527944
  • Title

    Minimum generation error training by using original spectrum as reference for log spectral distortion measure

  • Author

    Wu, Yi-Jian ; Tokuda, Keiichi

  • Author_Institution
    Nagoya Inst. of Technol., Nagoya
  • fYear
    2009
  • fDate
    19-24 April 2009
  • Firstpage
    4013
  • Lastpage
    4016
  • Abstract
    This paper improves a minimum generation error (MGE) based HMM training technique for HMM-based speech synthesis by directly using the original spectrum instead of line spectral pairs (LSPs) as reference spectrum for log spectral distortion (LSD) measure. Two types of original reference spectra for LSD calculation are investigated, including the spectrum extracted from speech waveform by STRAIGHT, and the short-time FFT spectrum calculated from speech waveforms. Since only the harmonics of the FFT spectrum are coincident with the underlying spectral envelope, the LSD between generated LSPs and original FFT spectrum is calculated by sampling at the harmonic frequencies, and a weighting function is designed to simulate the sampling strategy on LSPs. From the experimental results, the MGE-LSD training using the FFT spectrum as reference spectrum achieved the best performance.
  • Keywords
    distortion; fast Fourier transforms; hidden Markov models; spectral analysis; speech synthesis; HMM-based speech synthesis; log spectral distortion measure; minimum generation error training; original spectrum extraction; short-time FFT spectrum calculation; speech waveform; Distortion measurement; Euclidean distance; Frequency; Hidden Markov models; Sampling methods; Spectral analysis; Speech analysis; Speech processing; Speech synthesis; Training data; HMM; Speech synthesis; log spectral distortion; minimum generation error;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
  • Conference_Location
    Taipei
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4244-2353-8
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2009.4960508
  • Filename
    4960508