A review of state-of-the-art speech modelling methods for the parameterisation of expressive synthetic speech

This document will review a sample of available voice modelling and transformation techniques, in view of an application in expressive unit-selection based speech synthesis in the framework of the PAVOQUE project. The underlying idea is to introduce some parametric modification capabilities at the level of the synthesis system, in order to compensate for the sparsity and rigidity, in terms of available emotional speaking styles, of the databases used to define speech synthesis voices. For this work, emotion-related parametric modifications will be restricted to the domains of voice quality and prosody, as suggested by several reviews addressing the vocal correlates of emotions (Schröder, 2001; Schröder, 2004; Roehling et al., 2006). The present report will start with a review of some techniques related to voice quality modelling and modification. First, it will explore the techniques related to glottal flow modelling. Then, it will review the domain of cross-speaker voice transformations, in view of a transposition to the domain of cross-emotion voice transformations. This topic will be exposed from the perspective of the parametric spectral modelling of speech and then from the perspective of available spectral transformation techniques. Then, the domain of prosodic parameterisation and modification will be reviewed

Recent development of the HMM-based speech synthesis system (HTS)

A statistical parametric approach to speech synthesis based on hidden Markov models (HMMs) has grown in popularity over the last few years. In this approach, spectrum, excitation, and duration of speech are simultaneously modeled by context-dependent HMMs, and speech waveforms are generate from the HMMs themselves. Since December 2002, we have publicly released an open-source software toolkit named “HMM-based speech synthesis system (HTS)” to provide a research and development toolkit for statistical parametric speech synthesis. This paper describes recent developments of HTS in detail, as well as future release plans

Using eigenvoices and nearest-neighbours in HMM-based cross-lingual speaker adaptation with limited data

Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using limited adaptation data. To that end, we propose two eigenvoice adaptation approaches exploiting a bilingual Turkish-English speech database that we collected. In one approach, eigenvoice weights extracted using Turkish adaptation data and Turkish voice models are transformed into the eigenvoice weights for the English voice models using linear regression. Weighting the samples depending on the distance of reference speakers to target speakers during linear regression was found to improve the performance. Moreover, importance weighting the elements of the eigenvectors during regression further improved the performance. The second approach proposed here is speaker-specific state-mapping, which performed significantly better than the baseline state-mapping algorithm both in objective and subjective tests. Performance of the proposed state mapping algorithm was further improved when it was used with the intralingual eigenvoice approach instead of the linear-regression based algorithms used in the baseline system.European Commission ; TUBITA

Mixture of Factor Analyzers Using Priors from Non-Parallel Speech for Voice Conversion

Abstract—A robust voice conversion function relies on a large amount of parallel training data, which is difficult to collect in practice. To tackle the sparse parallel training data problem in voice conversion, this paper describes a mixture of factor ana-lyzers method which integrates prior knowledge from non-parallel speech into the training of conversion function. The experiments on CMU ARCTIC corpus show that the proposed method im-proves the quality and similarity of converted speech. With both objective and subjective evaluations, we show the proposed method outperforms the baseline GMM method. Index Terms—Voice conversion, prior knowledge, factor anal-ysis, mixture of factor analyzers. I