Discusses the differences between spoken and written forms of language, and describes the structure of a typical TTS system.
Taylor – Chapter 16 – Unit-selection synthesis
A substantial chapter covering target cost, join cost and search.
Taylor – Chapter 15 – Hidden-Markov-model synthesis
Written with a traditional “starting from automatic speech recognition” viewpoint, you will need to make the connections for yourself to the more general concept of text-to-speech as a regression problem.
Talkin: A Robust Algorithm for Pitch Tracking (RAPT)
The classic algorithm for estimating F0 from speech signals.
Tachibana et al. Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention
DCTTS is comparable to Tacotron, but is faster because it uses non-recurrent architectures for the encoder and decoder.
Shen et al: Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions
Tacotron 2 was one of the most successful sequence-to-sequence models for text-to-speech of its time and inspired many subsequent models.
Ren et al. FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
FastSpeech 2 improves over FastSpeech by not requiring a complicated teacher-student training regime, but instead being trained directly on the data. It is very similar to FastPitch 2, which was released around the same by different authors.
Qian et al: A Unified Trajectory Tiling Approach to High Quality Speech Rendering
The term “trajectory tiling” means that trajectories from a statistical model (HMMs in this case) are not input to a vocoder, but are “covered over” or “tiled” with waveform fragments.
Pollet & Breen: Synthesis by Generation and Concatenation of Multiform Segments
Another way to combine waveform concatenation and SPSS is to alternate between waveform fragments and vocoder-generated waveforms.
Norrenbrock et al: Quality prediction of synthesised speech…
Although standard speech quality measures such as PESQ do not work well for synthetic speech, specially constructed methods do work to some extent.
Nielsen: Neural Networks and Deep Learning
A great introduction. Relatively light on maths, and with some interactive explanations.
Mayo et al: Multidimensional scaling of listener responses to synthetic speech
Multi-dimensional scaling is a way to uncover the different perceptual dimensions that listeners use, when rating synthetic speech.
This is the new version. Still under construction.