Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the-art performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves the training efficiency. Meanwhile, any two inputs at different times are connected directly by self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).
Traditional speech synthesis methods can be categorized into two classes: concatenative systems and parametric systems. Concatenative TTS systems @cite_21 @cite_17 split original waves into small units, and stitch them by some algorithms such as Viterbi @cite_22 followed by signal process methods @cite_20 @cite_6 to generate new waves. Parametric TTS systems @cite_1 @cite_14 @cite_10 @cite_26 convert speech waves into spectrograms, and acoustic parameters, such as fundamental frequency and duration, are used to synthesize new audio results.
@cite_14 @cite_26 @cite_22 @cite_21 @cite_1 @cite_6 @cite_10 @cite_20 @cite_17
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HMM-TTS synthesis is a popular approach toward flexible, low-footprint, data driven systems that produce highly intelligible speech. In spite of these strengths, speech generated by these systems exhibit some degradation in quality, attributable to an inadequacy in modeling the excitation signal that drives the parametric models of the vocal tract. This paper proposes a novel method for modeling the excitation as a low-dimensional set of coefficients, based on a non-linear map learned through an autoencoder. Through analysis-and-resynthesis experiments, and a formal listening test, we show that this model produces speech of higher perceptual quality compared to conventional pulse-excited speech signals at the p ≪ 0.01 significance level. Current text-to-speech synthesis (TTS) systems are often perceived as lacking expressiveness, limiting the ability to fully convey information. This paper describes initial investigations into improving expressiveness for statistical speech synthesis systems. Rather than using hand-crafted definitions of expressive classes, an unsupervised clustering approach is described which is scalable to large quantities of training data. To incorporate this “expression cluster” information into an HMM-TTS system two approaches are described: cluster questions in the decision tree construction; and average expression speech synthesis (AESS) using cluster-based linear transform adaptation. The performance of the approaches was evaluated on audiobook data in which the reader exhibits a wide range of expressiveness. A subjective listening test showed that synthesising with AESS results in speech that better reflects the expressiveness of human speech than a baseline expression-independent system. We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation. One approach to the generation of natural-sounding synthesized speech waveforms is to select and concatenate units from a large speech database. Units (in the current work, phonemes) are selected to produce a natural realisation of a target phoneme sequence predicted from text which is annotated with prosodic and phonetic context information. We propose that the units in a synthesis database can be considered as a state transition network in which the state occupancy cost is the distance between a database unit and a target, and the transition cost is an estimate of the quality of concatenation of two consecutive units. This framework has many similarities to HMM-based speech recognition. A pruned Viterbi search is used to select the best units for synthesis from the database. This approach to waveform synthesis permits training from natural speech: two methods for training from speech are presented which provide weights which produce more natural speech than can be obtained by hand-tuning.