Search Results for author: Yi-Te Hsu

Found 6 papers, 0 papers with code

Generative Context-aware Fine-tuning of Self-supervised Speech Models

no code implementations15 Dec 2023 Suwon Shon, Kwangyoun Kim, Prashant Sridhar, Yi-Te Hsu, Shinji Watanabe, Karen Livescu

Considering the recent advances in generative large language models (LLM), we hypothesize that an LLM could generate useful context information using the preceding text.

Automatic Speech Recognition named-entity-recognition +6

SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points

no code implementations8 Nov 2021 Yu-Chen Lin, Cheng Yu, Yi-Te Hsu, Szu-Wei Fu, Yu Tsao, Tei-Wei Kuo

In this paper, a novel sign-exponent-only floating-point network (SEOFP-NET) technique is proposed to compress the model size and accelerate the inference time for speech enhancement, a regression task of speech signal processing.

Model Compression regression +1

Efficient Inference For Neural Machine Translation

no code implementations EMNLP (sustainlp) 2020 Yi-Te Hsu, Sarthak Garg, Yi-Hsiu Liao, Ilya Chatsviorkin

Large Transformer models have achieved state-of-the-art results in neural machine translation and have become standard in the field.

Machine Translation Translation

Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus

no code implementations NAACL 2019 Bai Li, Yi-Te Hsu, Frank Rudzicz

Machine learning has shown promise for automatic detection of Alzheimer's disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English.

BIG-bench Machine Learning Machine Translation +2

Robustness against the channel effect in pathological voice detection

no code implementations26 Nov 2018 Yi-Te Hsu, Zining Zhu, Chi-Te Wang, Shih-Hau Fang, Frank Rudzicz, Yu Tsao

In this study, we propose a detection system for pathological voice, which is robust against the channel effect.

Unsupervised Domain Adaptation

A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)

no code implementations17 Aug 2018 Yi-Te Hsu, Yu-Chen Lin, Szu-Wei Fu, Yu Tsao, Tei-Wei Kuo

We evaluated the proposed EOFP quantization technique on two types of neural networks, namely, bidirectional long short-term memory (BLSTM) and fully convolutional neural network (FCN), on a speech enhancement task.

Quantization regression +1

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