Search Results for author: Ju-chieh Chou

Found 9 papers, 6 papers with code

Few-Shot Spoken Language Understanding via Joint Speech-Text Models

no code implementations9 Oct 2023 Chung-Ming Chien, Mingjiamei Zhang, Ju-chieh Chou, Karen Livescu

Recent work on speech representation models jointly pre-trained with text has demonstrated the potential of improving speech representations by encoding speech and text in a shared space.

named-entity-recognition Named Entity Recognition +2

AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement

no code implementations14 Sep 2023 Ju-chieh Chou, Chung-Ming Chien, Karen Livescu

In this work, we introduce AV2Wav, a resynthesis-based audio-visual speech enhancement approach that can generate clean speech despite the challenges of real-world training data.

Resynthesis Speech Enhancement

Layer-wise Analysis of a Self-supervised Speech Representation Model

1 code implementation10 Jul 2021 Ankita Pasad, Ju-chieh Chou, Karen Livescu

Recently proposed self-supervised learning approaches have been successful for pre-training speech representation models.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization

10 code implementations10 Apr 2019 Ju-chieh Chou, Cheng-chieh Yeh, Hung-Yi Lee

Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-target scenario in which a single model is trained to convert the input voice to many different speakers.

Voice Conversion

Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

1 code implementation9 Aug 2018 Cheng-chieh Yeh, Po-chun Hsu, Ju-chieh Chou, Hung-Yi Lee, Lin-shan Lee

In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data.

Sound Audio and Speech Processing

Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations

4 code implementations9 Apr 2018 Ju-chieh Chou, Cheng-chieh Yeh, Hung-Yi Lee, Lin-shan Lee

The decoder then takes the speaker-independent latent representation and the target speaker embedding as the input to generate the voice of the target speaker with the linguistic content of the source utterance.

Voice Conversion

Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks

1 code implementation EMNLP 2017 Peng-Hsuan Li, Ruo-Ping Dong, Yu-Siang Wang, Ju-chieh Chou, Wei-Yun Ma

Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition.

named-entity-recognition Named Entity Recognition +1

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