Mouchtaris, Athanasios

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2006 - 20072
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  • Publication
    Nonparallel Training for Voice Conversion Based on a Parameter Adaptation Approach
    (2006-05-01) Mouchtaris, Athanasios; Van der Spiegel, Jan; Mueller, Paul
    The objective of voice conversion algorithms is to modify the speech by a particular source speaker so that it sounds as if spoken by a different target speaker. Current conversion algorithms employ a training procedure, during which the same utterances spoken by both the source and target speakers are needed for deriving the desired conversion parameters. Such a (parallel) corpus, is often difficult or impossible to collect. Here, we propose an algorithm that relaxes this constraint, i.e., the training corpus does not necessarily contain the same utterances from both speakers. The proposed algorithm is based on speaker adaptation techniques, adapting the conversion parameters derived for a particular pair of speakers to a different pair, for which only a nonparallel corpus is available. We show that adaptation reduces the error obtained when simply applying the conversion parameters of one pair of speakers to another by a factor that can reach 30%. A speaker identification measure is also employed that more insightfully portrays the importance of adaptation, while listening tests confirm the success of our method. Both the objective and subjective tests employed, demonstrate that the proposed algorithm achieves comparable results with the ideal case when a parallel corpus is available.
  • Publication
    A Spectral Conversion Approach to Single-Channel Speech Enhancement
    (2007-05-01) Mouchtaris, Athanasios; Van der Spiegel, Jan; Mueller, Paul; Tsakalides, Panagiotis
    In this paper, a novel method for single-channel speech enhancement is proposed, which is based on a spectral conversion feature denoising approach. Spectral conversion has been applied previously in the context of voice conversion, and has been shown to successfully transform spectral features with particular statistical properties into spectral features that best fit (with the constraint of a piecewise linear transformation) different target statistics. This spectral transformation is applied as an initialization step to two well-known single channel enhancement methods, namely the iterativeWiener filter (IWF) and a particular iterative implementation of the Kalman filter. In both cases, spectral conversion is shown here to provide a significant improvement as opposed to initializations using the spectral features directly from the noisy speech. In essence, the proposed approach allows for applying these two algorithms in a user-centric manner, when "clean" speech training data are available from a particular speaker. The extra step of spectral conversion is shown to offer significant advantages regarding output signal-to-noise ratio (SNR) improvement over the conventional initializations, which can reach 2 dB for the IWF and 6 dB for the Kalman filtering algorithm, for low input SNRs and for white and colored noise, respectively.