38 citations · 82 across the 15 of their papers we have counts for
15 papers
Laugh Now Cry Later: Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-to-Speech
Haibin Wu, Xiaofei Wang, Sefik Emre Eskimez +8
People change their tones of voice, often accompanied by nonverbal vocalizations (NVs) such as laughter and cries, to convey rich emotions. However, most text-to-speech (TTS) syste…
VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment
Bing Han, Long Zhou, Shujie Liu +7
With the help of discrete neural audio codecs, large language models (LLM) have increasingly been recognized as a promising methodology for zero-shot Text-to-Speech (TTS) synthesis…
An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS
Xiaofei Wang, Sefik Emre Eskimez, Manthan Thakker +8
Recently, zero-shot text-to-speech (TTS) systems, capable of synthesizing any speaker's voice from a short audio prompt, have made rapid advancements. However, the quality of the g…
Total-Duration-Aware Duration Modeling for Text-to-Speech Systems
Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker +9
Accurate control of the total duration of generated speech by adjusting the speech rate is crucial for various text-to-speech (TTS) applications. However, the impact of adjusting t…
Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like
Naoyuki Kanda, Xiaofei Wang, Sefik Emre Eskimez +12
Laughter is one of the most expressive and natural aspects of human speech, conveying emotions, social cues, and humor. However, most text-to-speech (TTS) systems lack the ability…
MuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023
Zhihang Xu, Shaofei Zhang, Xi Wang +4
In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023. About 50 hours of audiobook corpus for Fre…