Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural
speech given text, is a hot research topic in speech, language, and machine learning …

Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize
speech significantly faster than previous autoregressive models with comparable quality …

Style transfer for out-of-domain (OOD) speech synthesis aims to generate speech samples
with unseen style (eg, speaker identity, emotion, and prosody) derived from an acoustic …

With rapid progress in neural text-to-speech (TTS) models, personalized speech generation
is now in high demand for many applications. For practical applicability, a TTS model should …

Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims
to adapt a source TTS model to synthesize personal voice for a target speaker using few …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

High-fidelity multi-singer singing voice synthesis is challenging for neural vocoder due to the
singing voice data shortage, limited singer generalization, and large computational cost …

Adaptive text to speech (TTS) can synthesize new voices in zero-shot scenarios efficiently,
by using a well-trained source TTS model without adapting it on the speech data of new …

Speech conveys more information than just text, as the same word can be uttered in various
voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods …