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Found 13 papers, 7 papers with code
PodAgent: A Comprehensive Framework for Podcast Generation
The key challenges lie in in-depth content generation, appropriate and expressive voice production.
Audio-FLAN: A Preliminary Release
Audio-FLAN lays the foundation for unified audio-language models that can seamlessly handle both understanding (e. g., transcription, comprehension) and generation (e. g., speech, music, sound) tasks across a wide range of audio domains in a zero-shot manner.
SimpleSpeech 2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion Models
With these improvement, we show a significant improvement in generation performance and generation speed compared to our previous work and other state-of-the-art (SOTA) large-scale TTS models.
Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation
The multi-codebook speech codec enables the application of large language models (LLM) in TTS but bottlenecks efficiency and robustness due to multi-sequence prediction.
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Echoing the widely-reported "emergent abilities" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences.
Cross-Speaker Encoding Network for Multi-Talker Speech Recognition
To the best of our knowledge, this work represents an early effort to integrate SIMO and SISO for multi-talker speech recognition.
QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning
This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS quality with lower supervised data requirements via Vector-Quantized Self-Supervised Speech Representation Learning (VQ-S3RL) utilizing more unlabeled speech audio.
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations
Moreover, we optimize the training strategy by leveraging more audio to learn MSMCRs better for low-resource languages.
A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS
A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is used to encode Mel spectrograms of speech training data by down-sampling progressively in multiple stages into MSMC Representations (MSMCRs) with different time resolutions, and quantizing them with multiple VQ codebooks, respectively.
Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training
In order to make timbre conversion more stable and controllable, speaker embedding is further decomposed to the weighted sum of a group of trainable vectors representing different timbre clusters.
Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS
The end-to-end TTS, which can predict speech directly from a given sequence of graphemes or phonemes, has shown improved performance over the conventional TTS.
A New GAN-based End-to-End TTS Training Algorithm
However, the autoregressive module training is affected by the exposure bias, or the mismatch between the different distributions of real and predicted data.
Feature reinforcement with word embedding and parsing information in neural TTS
In this paper, we propose a feature reinforcement method under the sequence-to-sequence neural text-to-speech (TTS) synthesis framework.
