Sentence examples for voice conversion system from inspiring English sources

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors.

The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.

Figure 1 A flow chart of the proposed voice conversion system.

Figure 1 shows an overview of our proposed voice conversion system where we set P=1.

In [9], the spectrum and duration are converted in a voice conversion system with duration-embedded hidden Markov models (HMMs).

The framework of voice conversion system is expected to emphasize both the static and dynamic characteristics of the speech signal.

Voice conversion systems aim to modify the perceived identity of a source speaker saying a sentence to that of a given target speaker.

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The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures.

A voice conversion (VC) system was designed based on Gaussian mixture model (GMM) and radial basis function (RBF) neural network.

The voice conversion (VC) system aims to apply various modifications to the source speaker's voice so that the converted signal sounds like a particular target speaker's voice[1, 2].