Search Results for author: Ning Dong

Found 10 papers, 2 papers with code

Exploring Speech Enhancement for Low-resource Speech Synthesis

no code implementations19 Sep 2023 Zhaoheng Ni, Sravya Popuri, Ning Dong, Kohei Saijo, Xiaohui Zhang, Gael Le Lan, Yangyang Shi, Vikas Chandra, Changhan Wang

High-quality and intelligible speech is essential to text-to-speech (TTS) model training, however, obtaining high-quality data for low-resource languages is challenging and expensive.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Multilingual Speech-to-Speech Translation into Multiple Target Languages

no code implementations17 Jul 2023 Hongyu Gong, Ning Dong, Sravya Popuri, Vedanuj Goswami, Ann Lee, Juan Pino

Despite a few studies on multilingual S2ST, their focus is the multilinguality on the source side, i. e., the translation from multiple source languages to one target language.

Language Identification Speech-to-Speech Translation +1

Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks

no code implementations4 May 2023 Yun Tang, Anna Y. Sun, Hirofumi Inaguma, Xinyue Chen, Ning Dong, Xutai Ma, Paden D. Tomasello, Juan Pino

In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation

no code implementations ACL 2020 Arya D. McCarthy, Xi-An Li, Jiatao Gu, Ning Dong

This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs).

de-en Machine Translation +2

Improved Variational Neural Machine Translation by Promoting Mutual Information

no code implementations19 Sep 2019 Arya D. McCarthy, Xi-An Li, Jiatao Gu, Ning Dong

Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders.

Conditional Text Generation Decoder +2

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