In search of the optimal acoustic features for statistical parametric speech synthesis

In the Statistical Parametric Speech Synthesis (SPSS) paradigm, speech is generally represented as acoustic features and the waveform is generated by a vocoder. A comprehensive summary of state-of-the-art vocoding techniques is presented, highlighting their characteristics, advantages, and drawbacks, primarily when used in SPSS. We conclude that state-of-the-art vocoding methods are suboptimal and are a cause of significant loss of quality, even though numerous vocoders have been proposed in the last decade. In fact, it seems that the most complicated methods perform worse than simpler ones based on more robust analysis/synthesis algorithms. Typical methods, based on the source-filter or sinusoidal models, rely on excessive simplifying assumptions. They perform what we call an "extreme decomposition" of speech (e.g., source+filter or sinusoids+ noise), which we believe to be a major drawback. Problems include: difficulties in the estimation of components; modelling of complex non-linear mechanisms; a lack of ground truth. In addition, the statistical dependence that exists between stochastic and deterministic components of speech is not modelled. We start by improving just the waveform generation stage of SPSS, using standard acoustic features. We propose a new method of waveform generation tailored for SPSS, based on neither source-filter separation nor sinusoidal modelling. The proposed waveform generator avoids unnecessary assumptions and decompositions as far as possible, and uses only the fundamental frequency and spectral envelope as acoustic features. A very small speech database is used as a source of base speech signals which are subsequently \reshaped" to match the specifications output by the acoustic model in the SPSS framework. All of this is done without any decomposition, such as source+filter or harmonics+noise. A comprehensive description of the waveform generation process is presented, along with implementation issues. Two SPSS voices, a female and a male, were built to test the proposed method by using a standard TTS toolkit, Merlin. In a subjective evaluation, listeners preferred the proposed waveform generator over a state-of-the-art vocoder, STRAIGHT. Even though the proposed \waveform reshaping" generator generates higher speech quality than STRAIGHT, the improvement is not large enough. Consequently, we propose a new acoustic representation, whose implementation involves feature extraction and waveform generation, i.e., a complete vocoder. The new representation encodes the complex spectrum derived from the Fourier Transform in a way explicitly designed for SPSS, rather than for speech coding or copy-synthesis. The feature set comprises four feature streams describing magnitude spectrum, phase spectrum, and fundamental frequency; all of these are represented by real numbers. It avoids heuristics or unstable methods for phase unwrapping. The new feature extraction does not attempt to decompose the speech structure and thus the "phasiness" and "buzziness" found in a typical vocoder, such as STRAIGHT, is dramatically reduced. Our method works at a lower frame rate than a typical vocoder. To demonstrate the proposed method, two DNN-based voices, a male and a female, were built using the Merlin toolkit. Subjective comparisons were performed with a state-of-the-art baseline. The proposed vocoder substantially outperformed the baseline for both voices and under all configurations tested. Furthermore, several enhancements were made over the original design, which are beneficial for either sound quality or compatibility with other tools. In addition to its use in SPSS, the proposed vocoder is also demonstrated being used for join smoothing in unit selection-based systems, and can be used for voice conversion or automatic speech recognition