Text-to-speech (TTS) synthesis is the art of designing talking machines. Seen from this functional perspective, the task looks simple, but this chapter shows that delivering intelligible, natural-sounding, and expressive speech, while also taking into account engineering costs, is a real challenge. Speech synthesis has made a long journey from the big controversy in the 1980s, between MIT’s formant synthesis and Bell Labs’ diphone-based concatenative synthesis. While unit selection technology, which appeared in the mid-1990s, can be seen as an extension of diphone-based approaches, the appearance of Hidden Markov Models (HMM) synthesis around 2005 resulted in a major shift back to models. More recently, the statistical approaches, supported by advanced deep learning architectures, have been shown to advance text analysis and normalization as well as the generation of the waveforms. Important recent milestones have been Google’s Wavenet (September 2016) and the sequence-to-sequence models referred to as Tacotron (I and II).
Integration of rule-based formant synthesis and waveform concatenation: a hybrid approach to text-to-speech synthesis
