Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Thakker, Manthan"'
Autor:
Eskimez, Sefik Emre, Wang, Xiaofei, Thakker, Manthan, Li, Canrun, Tsai, Chung-Hsien, Xiao, Zhen, Yang, Hemin, Zhu, Zirun, Tang, Min, Tan, Xu, Liu, Yanqing, Zhao, Sheng, Kanda, Naoyuki
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-art speaker similarity and intelligibility. In the E2 TTS framework, th
Externí odkaz:
http://arxiv.org/abs/2406.18009
Autor:
Wang, Xiaofei, Eskimez, Sefik Emre, Thakker, Manthan, Yang, Hemin, Zhu, Zirun, Tang, Min, Xia, Yufei, Li, Jinzhu, Zhao, Sheng, Li, Jinyu, Kanda, Naoyuki
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 generated speech significantly deteriorates when the audio prompt conta
Externí odkaz:
http://arxiv.org/abs/2406.05699
Autor:
Eskimez, Sefik Emre, Wang, Xiaofei, Thakker, Manthan, Tsai, Chung-Hsien, Li, Canrun, Xiao, Zhen, Yang, Hemin, Zhu, Zirun, Tang, Min, Li, Jinyu, Zhao, Sheng, Kanda, Naoyuki
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 the speech rate on speech quality, such as intelligibility and speaker
Externí odkaz:
http://arxiv.org/abs/2406.04281
Autor:
Kanda, Naoyuki, Wang, Xiaofei, Eskimez, Sefik Emre, Thakker, Manthan, Yang, Hemin, Zhu, Zirun, Tang, Min, Li, Canrun, Tsai, Chung-Hsien, Xiao, Zhen, Xia, Yufei, Li, Jinzhu, Liu, Yanqing, Zhao, Sheng, Zeng, Michael
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 to produce realistic and appropriate laughter sounds, limiting their a
Externí odkaz:
http://arxiv.org/abs/2402.07383
Autor:
Wang, Xiaofei, Thakker, Manthan, Chen, Zhuo, Kanda, Naoyuki, Eskimez, Sefik Emre, Chen, Sanyuan, Tang, Min, Liu, Shujie, Li, Jinyu, Yoshioka, Takuya
Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models still face limitations in handling diverse audio-text speech generati
Externí odkaz:
http://arxiv.org/abs/2308.06873
This paper investigates how to improve the runtime speed of personalized speech enhancement (PSE) networks while maintaining the model quality. Our approach includes two aspects: architecture and knowledge distillation (KD). We propose an end-to-end
Externí odkaz:
http://arxiv.org/abs/2204.00771
Autor:
Dubey, Harishchandra, Gopal, Vishak, Cutler, Ross, Aazami, Ashkan, Matusevych, Sergiy, Braun, Sebastian, Eskimez, Sefik Emre, Thakker, Manthan, Yoshioka, Takuya, Gamper, Hannes, Aichner, Robert
The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. This is the 4th DNS challenge, with the previous editions held at INTERSPEECH 2020, ICASSP 202
Externí odkaz:
http://arxiv.org/abs/2202.13288