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 …

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 …

While speaker adaptation for end-to-end speech synthesis using speaker embeddings can
produce good speaker similarity for speakers seen during training, there remains a gap for …

On account of growing demands for personalization, the need for a so-called few-shot TTS
system that clones speakers with only a few data is emerging. To address this issue, we …

Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly
extracts intents from speech requires large amounts of intent-labeled speech data, which is …

The availability of smart devices leads to an exponential increase in multimedia content.
However, the rapid advancements in deep learning have given rise to sophisticated …

Zero-shot text-to-speech (TTS) aims to synthesize voices with unseen speech prompts,
which significantly reduces the data and computation requirements for voice cloning by …

Recent advances in neural multi-speaker text-to-speech (TTS) models have enabled the
generation of reasonably good speech quality with a single model and made it possible to …

Multi-speaker text-to-speech (TTS) using a few adaption data is a challenge in practical
applications. To address that, we propose a zero-shot multi-speaker TTS, named nnSpeech …

State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data
to generate high-quality synthetic speech. When using reduced amounts of training data …