Deep learning speech synthesis
Deep learning speech synthesis uses Deep Neural Networks (DNN) to produce artificial speech from text (text-to-speech) or spectrum (vocoder). The deep neural networks are trained using a large amount of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text. Some DNN-based speech synthesizers are approaching the naturalness of the human voice. Formulation. Given an input text or some sequence of linguistic unit formula_1, the target speech formula_2 can be derived by formula_3 where formula_4 is the model parameter. Typically, the input text will first  be passed to an acoustic feature generator, then the acoustic features are passed to the neural vocoder. For the acoustic feature generator, the Loss function is typically L1 or L2 loss. These loss functions impose a constraint that the output acoustic feature distributions must be Gaussian or Laplacian. In practice, since the human voice band ranges from approximately 300 to 4000 Hz, the loss function will be designed to have more penalty on this range: formula_5 where formula_6 is the loss from human voice band and formula_7 is a scalar typically around 0.5. The acoustic feature is typically Spectrogram or  spectrogram in Mel scale. These features capture the time-frequency relation of speech signal and thus, it is sufficient to generate intelligent outputs with these acoustic features. The Mel-frequency cepstrum feature used in the speech recognition task is not suitable for speech synthesis because it reduces too much information. Brief history. In September 2016, DeepMind proposed WaveNet, a deep generative model of raw audio waveforms. This shows the community that deep learning-based models have the capability to model raw waveforms and perform well on generating speech from acoustic features like spectrograms or spectrograms in mel scale, or even  from some preprocessed linguistic features. In early 2017, Mila (research institute) proposed char2wav, a model to produce raw waveform in an end-to-end method. Also, Google and Facebook proposed Tacotron and VoiceLoop, respectively, to generate acoustic features directly from the input text. In the later in the same year, Google proposed Tacotron2 which combined the WaveNet vocoder with the revised Tacotron architecture to perform end-to-end speech synthesis. Tacotron2 can generate high-quality speech approaching the human voice. Since then, end-to-end methods have become the hottest research topic because many researchers around the world have started  to notice the power of end-to-end speech synthesizers. Semi-supervised learning. Currently, self-supervised learning has gained much attention through better use of unlabelled data. Research has shown that, with the aid of self-supervised loss, the need for paired data decreases. Zero-shot speaker adaptation. Zero-shot speaker adaptation is promising because a single model can generate speech with various speaker styles and characteristic. In June 2018, Google proposed to use pre-trained speaker verification models as speaker encoders to extract speaker embeddings. The speaker encoders then become part of the neural text-to-speech models, so  that it can determine the style and characteristics of the output speech. This procedure has shown the community that it is possible to use only a single model to generate speech with multiple styles. Neural vocoder. In deep learning-based speech synthesis, neural vocoders play an important role in generating high-quality speech from acoustic features. The WaveNet model proposed in 2016 achieves excellent performance on speech quality. Wavenet factorised the joint probability of a waveform formula_8 as a product of conditional probabilities as follows formula_9 where formula_4 is the model parameter including many dilated convolution layers. Thus, each audio  sample formula_11 is conditioned on the samples at all previous timesteps. However, the auto-regressive nature of WaveNet makes the inference process dramatically slow. To solve this problem, Parallel WaveNet was proposed. Parallel WaveNet is an inverse autoregressive flow-based model which is trained by knowledge distillation with a pre-trained teacher WaveNet model. Since such inverse autoregressive flow-based models are non-auto-regressive when performing inference, the inference speed is faster than real-time. Meanwhile, Nvidia proposed a flow-based WaveGlow model, which can also generate speech faster than real-time. However, despite the high inference speed, parallel  WaveNet has the limitation of needing a pre-trained WaveNet model, so that WaveGlow takes many weeks to converge with limited computing devices. This issue has been solved by Parallel WaveGAN, which learns to produce speech through multi-resolution spectral loss and GAN learning strategies.