We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.
Our work is not the first to attempt a multi-speaker TTS system. For instance, in traditional HMM-based TTS synthesis [e.g.,][] yamagishi2009robust , an average voice model is trained using multiple speakers' data, which is then adapted to different speakers. DNN-based systems [e.g.,][] yang2016training have also been used to build average voice models, with i-vectors representing speakers as additional inputs and separate output layers for each target speaker. Similarly, @cite_1 uses a shared hidden representation among different speakers with speaker-dependent output layers predicting vocoder parameters (e.g., line spectral pairs, aperiodicity parameters etc.). For further context, @cite_0 empirically studies DNN-based multi-speaker modeling. More recently, speaker adaptation has been tackled with generative adversarial networks (GANs) .
@cite_0 @cite_1
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Recently, speaker adaptation of neural TTS models received significant interest, and several studies focusing on this topic have been published. All of them explore an adaptation of an initial multi-speaker model trained on a corpus containing from tens to hundreds of individual speaker voices.In this work we focus on a challenging task of TTS voice conversion where an initial system is trained on a single-speaker data and then need to be adapted to a variety of external speaker voices. The TTS voice conversion setup represents a very important use case. Transcribed multi-speaker datasets might be unavailable for many languages while any TTS technology provider is expected to have at least one suitable single-speaker dataset per supported language.We present a neural TTS system comprising separate prosody generator and synthesizer DNN models. The system is trained on a high quality proprietary male speaker dataset. We show that the system models can be converted to a variety of external male and female ordinary voices and an extremely expressive artist’s voice and present crowd-base subjective evaluation results. We have successfully proposed to use multi-speaker modelling in DNN-based TTS synthesis for improved voice quality with limited available data from a speaker. In this paper, we propose a new speaker and language factorized DNN, where speaker-specific layers are used for multi-speaker modelling, and shared layers and language-specific layers are employed for multi-language, linguistic feature transformation. Experimental results on a speech corpus of multiple speakers in both Mandarin and English show that the proposed factorized DNN can not only achieve a similar voice quality as that of a multi-speaker DNN, but also perform polyglot synthesis with a monolingual speaker's voice. We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies: (i) learning the speaker embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire architecture with stochastic gradient descent, and (iii) predicting the speaker embedding with a trained neural network encoder. The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers. Deep Neural Network (DNN), which can model a long-span, intricate transform compactly with a deep-layered structure, has recently been investigated for parametric TTS synthesis with a fairly large corpus (33,000 utterances) [6]. In this paper, we examine DNN TTS synthesis with a moderate size corpus of 5 hours, which is more commonly used for parametric TTS training. DNN is used to map input text features into output acoustic features (LSP, F0 and V U). Experimental results show that DNN can outperform the conventional HMM, which is trained in ML first and then refined by MGE. Both objective and subjective measures indicate that DNN can synthesize speech better than HMM-based baseline. The improvement is mainly on the prosody, i.e., the RMSE of natural and generated F0 trajectories by DNN is improved by 2 Hz. This benefit is likely from the key characteristics of DNN, which can exploit feature correlations, e.g., between F0 and spectrum, without using a more restricted, e.g. diagonal Gaussian probability family. Our experimental results also show: the layer-wise BP pre-training can drive weights to a better starting point than random initialization and result in a more effective DNN; state boundary info is important for training DNN to yield better synthesized speech; and a hyperbolic tangent activation function in DNN hidden layers yields faster convergence than a sigmoidal one.