Statistical Parametric Synthesizers Research Articles

On the impact of excitation and spectral parameters for expressive statistical parametric speech synthesis

This paper presents a study on the importance of short-term speech parameterizations for expressive statistical parametric synthesis. Assuming a source-filter model of speech production, the analysis is conducted over spectral parameters, here defined as features which represent a minimum-phase synthesis filter, and some excitation parameters, which are features used to construct a signal that is fed to the minimum-phase synthesis filter to generate speech. In the first part, different spectral and excitation parameters that are applicable to statistical parametric synthesis are tested to determine which ones are the most emotion dependent. The analysis is performed through two methods proposed to measure the relative emotion dependency of each feature: one based on K-means clustering, and another based on Gaussian mixture modeling for emotion identification. Two commonly used forms of parameters for the short-term speech spectral envelope, the Mel cepstrum and the Mel line spectrum pairs are utilized. As excitation parameters, the anti-causal cepstrum, the time-smoothed group delay, and band-aperiodicity coefficients are considered. According to the analysis, the line spectral pairs are the most emotion dependent parameters. Among the excitation features, the band-aperiodicity coefficients present the highest correlation with the speaker's emotion. The most emotion dependent parameters according to this analysis were selected to train an expressive statistical parametric synthesizer using a speaker and language factorization framework. Subjective test results indicate that the considered spectral parameters have a bigger impact on the synthesized speech emotion when compared with the excitation ones.

Complex cepstrum for statistical parametric speech synthesis

Statistical parametric synthesizers have typically relied on a simplified model of speech production. In this model, speech is generated using a minimum-phase filter, implemented from coefficients derived from spectral parameters, driven by a zero or random phase excitation signal. This excitation signal is usually constructed from fundamental frequencies and parameters used to control the balance between the periodicity and aperiodicity of the signal. The application of this approach to statistical parametric synthesis has partly been motivated by speech coding theory. However, in contrast to most real-time speech coders, parametric speech synthesizers do not require causality. This allows the standard simplified model to be extended to represent the natural mixed-phase characteristics of speech signals. This paper proposes the use of the complex cepstrum to model the mixed phase characteristics of speech through the incorporation of phase information in statistical parametric synthesis. The phase information is contained in the anti-causal portion of the complex cepstrum. These parameters have a direct connection with the shape of the glottal pulse of the excitation signal. Phase parameters are extracted on a frame-basis and are modeled in the same fashion as the minimum-phase synthesis filter parameters. At synthesis time, phase parameter trajectories are generated and used to modify the excitation signal. Experimental results show that the use of such complex cepstrum-based phase features results in better synthesized speech quality. Listening test results yield an average preference of 60% for the system with the proposed phase feature on both female and male voices.

Synthesis of Child Speech With HMM Adaptation and Voice Conversion

The synthesis of child speech presents challenges both in the collection of data and in the building of a synthesizer from that data. We chose to build a statistical parametric synthesizer using the hidden Markov model (HMM)-based system HTS, as this technique has previously been shown to perform well for limited amounts of data, and for data collected under imperfect conditions. Six different configurations of the synthesizer were compared, using both speaker-dependent and speaker-adaptive modeling techniques, and using varying amounts of data. For comparison with HMM adaptation, techniques from voice conversion were used to transform existing synthesizers to the characteristics of the target speaker. Speaker-adaptive voices generally outperformed child speaker-dependent voices in the evaluation. HMM adaptation outperformed voice conversion style techniques when using the full target speaker corpus; with fewer adaptation data, however, no significant listener preference for either HMM adaptation or voice conversion methods was found.