Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like

no code implementations • 12 Feb 2024 • Naoyuki Kanda, Xiaofei Wang, Sefik Emre Eskimez, Manthan Thakker, Hemin Yang, Zirun Zhu, Min Tang, Canrun Li, Chung-Hsien Tsai, Zhen Xiao, Yufei Xia, Jinzhu Li, Yanqing Liu, Sheng Zhao, Michael Zeng

In this work, we propose ELaTE, a zero-shot TTS that can generate natural laughing speech of any speaker based on a short audio prompt with precise control of laughter timing and expression.

SpeechX: Neural Codec Language Model as a Versatile Speech Transformer

no code implementations • 14 Aug 2023 • Xiaofei Wang, Manthan Thakker, Zhuo Chen, Naoyuki Kanda, Sefik Emre Eskimez, Sanyuan Chen, Min Tang, Shujie Liu, Jinyu Li, Takuya Yoshioka

Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech.

Language Modelling Multi-Task Learning +2

Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation

no code implementations • 2 Apr 2022 • Manthan Thakker, Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang

Our results show that E3Net provides better speech and transcription quality with a lower target speaker over-suppression (TSOS) rate than the baseline model.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

ICASSP 2022 Deep Noise Suppression Challenge

1 code implementation • 27 Feb 2022 • Harishchandra Dubey, Vishak Gopal, Ross Cutler, Ashkan Aazami, Sergiy Matusevych, Sebastian Braun, Sefik Emre Eskimez, Manthan Thakker, Takuya Yoshioka, Hannes Gamper, Robert Aichner

We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P. 835 to rate and rank-order the challenge entries.