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Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps…
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Tacotron: Towards End-to-End Speech Synthesis

Tacotron is presented, an end-to-end generative text- to-speech model that synthesizes speech directly from characters that achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness.
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On Training Targets for Supervised Speech Separation

Results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics, and that masking based targets, in general, are significantly better than spectral envelope based targets.
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PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

This paper proposes pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset, and investigates the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks.
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Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

"global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system, learn to factorize noise and speaker identity, providing a path towards highly scalable but robust speech synthesis.
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Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

An extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody results in synthesized audio that matches the prosody of the reference signal with fine time detail.
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Complex Ratio Masking for Monaural Speech Separation

The proposed approach improves over other methods when evaluated with several objective metrics, including the perceptual evaluation of speech quality (PESQ), and a listening test where subjects prefer the proposed approach with at least a 69% rate.
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Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model

This paper presents Tacotron, an end- to-end generative text-to-speech model that synthesizes speech directly from characters, and presents several key techniques to make the sequence-tosequence framework perform well for this challenging task.

Trainable frontend for robust and far-field keyword spotting

This work introduces a novel frontend called per-channel energy normalization (PCEN), which uses an automatic gain control based dynamic compression to replace the widely used static compression in speech recognition.
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Hierarchical Generative Modeling for Controllable Speech Synthesis

A high-quality controllable TTS model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions is proposed.
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