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Found 16 papers, 7 papers with code
Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM
Key to our approach is a training objective that jointly supervises speech recognition, text continuation, and speech synthesis using only paired speech-text pairs, enabling a `cross-modal' chain-of-thought within a single decoding pass.
Learning the joint distribution of two sequences using little or no paired data
We present a noisy channel generative model of two sequences, for example text and speech, which enables uncovering the association between the two modalities when limited paired data is available.
Speaker Generation
We call this task "speaker generation", and present TacoSpawn, a system that performs competitively at this task.
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis
We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs.
Non-saturating GAN training as divergence minimization
Non-saturating generative adversarial network (GAN) training is widely used and has continued to obtain groundbreaking results.
Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis
Despite the ability to produce human-level speech for in-domain text, attention-based end-to-end text-to-speech (TTS) systems suffer from text alignment failures that increase in frequency for out-of-domain text.
Semi-Supervised Generative Modeling for Controllable Speech Synthesis
We present a novel generative model that combines state-of-the-art neural text-to-speech (TTS) with semi-supervised probabilistic latent variable models.
Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages.
Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis
Recent work has explored sequence-to-sequence latent variable models for expressive speech synthesis (supporting control and transfer of prosody and style), but has not presented a coherent framework for understanding the trade-offs between the competing methods.
Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis
We demonstrate that the proposed framework enables Tacotron to generate intelligible speech using less than half an hour of paired training data.
Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis
GSTs can be used within Tacotron, a state-of-the-art end-to-end text-to-speech synthesis system, to uncover expressive factors of variation in speaking style.
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
We present 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.
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
In this work, we propose "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.
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.
Ranked #2 on
Speech Synthesis
on North American English
Uncovering Latent Style Factors for Expressive Speech Synthesis
Prosodic modeling is a core problem in speech synthesis.
Tacotron: Towards End-to-End Speech Synthesis
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.
Ranked #5 on
Speech Synthesis
on North American English
