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

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

eess.AS2024

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…

eess.AS2024

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…

eess.AS2024

E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS

Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker +10

This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully non-autoregressive zero-shot text-to-speech system that offers human-level naturalness and state-of-the-a…

eess.AS2024★ 1 cited

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…

cs.SD2024

NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription

Alon Vinnikov, Amir Ivry, Aviv Hurvitz +16

We introduce the first Natural Office Talkers in Settings of Far-field Audio Recordings (``NOTSOFAR-1'') Challenge alongside datasets and baseline system. The challenge focuses on…