Search

This paper proposes an average voice model training technique based on a speaker clustering approach to generate synthetic speech with enhanced similarity to the target speakers’ speech. A novel point of the proposed technique is the use of the speaker characteristics (called “speaker class”), which are obtained from unsupervised clustering, as the additional contextual factor for the average voice based speech synthesis. In the model training process, first, speaker clustering is performed for all speakers used for model training to obtain the speaker class for each speaker. The average voice model with multiple speaker characteristics is trained by using the obtained speaker class. For the speaker adaptation and speech parameter generation, the speaker class of the target speaker is estimated on the basis of the Euclidean distance between the centroids of each cluster and the target speaker’s feature. The use of the estimated speaker class makes it possible to utilize the model parameters that have speaker characteristics similar to those of the target speaker for speaker adaptation and speech parameter generation. The results of objective and subjective experiments indicated the proposed technique can synthesize speech with improved similarity and naturalness. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a technique for adding prosodic variations in HMM-based speech synthesis. [•] Phrase-level contexts are defined that compensate the lack of prosodic variations. [•] Average prosodic difference between original and synthetic speech is used. [•] Expressive speech of sales talk and fairy tale is used for evaluation. [•] Users can manually change phrase-level pitch without degrading naturalness. [Copyright &y& Elsevier]

Abstract: This paper analyzes the impacts of machine translation and speech synthesis on speech-to-speech translation systems. A typical speech-to-speech translation system consists of three components: speech recognition, machine translation and speech synthesis. Many techniques have been proposed for integration of speech recognition and machine translation. However, corresponding techniques have not yet been considered for speech synthesis. The focus of the current work is machine translation and speech synthesis, and we present a subjective evaluation designed to analyze their impact on speech-to-speech translation. The results of these analyses show that the naturalness and intelligibility of the synthesized speech are strongly affected by the fluency of the translated sentences. In addition, several features were found to correlate well with the average fluency of the translated sentences and the average naturalness of the synthesized speech. [Copyright &y& Elsevier]

Abstract: The quality of current commercial speech synthesis systems is now so high that system improvements are being made at subtle sub- and supra-segmental levels. Human perceptual evaluation of such subtle improvements requires a highly sophisticated level of perceptual attention to specific acoustic characteristics or cues. However, it is not well understood what acoustic cues listeners attend to by default when asked to evaluate synthetic speech. It may, therefore, be potentially quite difficult to design an evaluation method that allows listeners to concentrate on only one dimension of the signal, while ignoring others that are perceptually more important to them. The aim of the current study was to determine which acoustic characteristics of unit-selection synthetic speech are most salient to listeners when evaluating the naturalness of such speech. This study made use of multidimensional scaling techniques to analyse listeners’ pairwise comparisons of synthetic speech sentences. Results indicate that listeners place a great deal of perceptual importance on the presence of artifacts and discontinuities in the speech, somewhat less importance on aspects of segmental quality, and very little importance on stress/intonation appropriateness. These relative differences in importance will impact on listeners’ ability to attend to these different acoustic characteristics of synthetic speech, and should therefore be taken into account when designing appropriate methods of synthetic speech evaluation. [Copyright &y& Elsevier]