Papers by Wei-Ning Hsu
Text-Free Image-to-Speech Synthesis Using Learned Segmental Units (2021.acl-long)
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| Challenge: | Existing models for synthesising fluent, natural-sounding spoken audio captions do not require natural language text as an intermediate representation or source of supervision. |
| Approach: | They propose a model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. |
| Outcome: | The proposed model captures diverse visual semantics of images and can replace text with a set of discrete, sub-word speech units. |
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit (2021.emnlp-demo)
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| Challenge: | Speech synthesis is the task of generating speech waveforms with desired characteristics, including but not limited to textual content, speaker identity, and speaking styles. |
| Approach: | They propose a fairseq extension for speech synthesis that implements autoregressive and non-AR text-to-speech models and their multi-speaker variants. |
| Outcome: | The proposed extension can train autoregressive and non-AR models and their multi-speaker variants with less curated data and has automatic metrics to facilitate faster iteration and analysis. |
Text-Free Prosody-Aware Generative Spoken Language Modeling (2022.acl-long)
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Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Morgane Riviere, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
| Challenge: | Experimental results show that generative spoken language models (LMs) are natural unsupervised multitask learners. |
| Approach: | They propose a prosody-aware generative spoken language model that uses discovered units to generate natural, meaningful, and coherent speech. |
| Outcome: | The proposed model can generate natural, meaningful, and coherent speech given a spoken prompt. |
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)
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Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee
| Challenge: | a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data. |
| Approach: | They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language . |
| Outcome: | The proposed system can be used to train models in languages without standard writing systems. |
Textless Speech-to-Speech Translation on Real Data (2022.naacl-main)
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Ann Lee, Hongyu Gong, Paul-Ambroise Duquenne, Holger Schwenk, Peng-Jen Chen, Changhan Wang, Sravya Popuri, Yossi Adi, Juan Pino, Jiatao Gu, Wei-Ning Hsu
| Challenge: | Existing text-based speech-to-speech translation systems rely on cascaded approach . text-to text translation systems require text generation and a single input to generate output . |
| Approach: | They propose a textless speech-to-speech translation system that can translate speech from one language into another without the need of text data. |
| Outcome: | The proposed system can translate speech from one language into another without text data. |
Direct Speech-to-Speech Translation With Discrete Units (2022.acl-long)
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Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Sravya Popuri, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu
| Challenge: | Existing direct speech-to-speech translation models rely on text generation as an intermediate step. |
| Approach: | They propose a direct speech-to-speech translation model that translates speech from one language to another without relying on intermediate text generation. |
| Outcome: | The proposed model produces 6.7 BLEUs in the Fisher Spanish-English dataset when trained without any text transcripts and with text supervision. |
textless-lib: a Library for Textless Spoken Language Processing (2022.naacl-demo)
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Eugene Kharitonov, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Paden Tomasello, Ann Lee, Ali Elkahky, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
| Challenge: | Textless spoken language processing is an exciting area of research that promises to extend applicability of the standard NLP toolset onto spoken language and languages with few or no textual resources. |
| Approach: | They introduce textless-lib, a PyTorch-based library that provides textless spoken language processing tools. |
| Outcome: | The proposed library significantly simplifies research in the textless setting and will be a handful for speech researchers and the NLP community at large. |
Generative Spoken Dialogue Language Modeling (2023.tacl-1)
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Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoît Sagot, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | dGSLM is the first “textless” model able to generate audio samples of naturalistic spoken dialogues. |
| Approach: | They propose a model that generates speech, laughter, and other paralinguistic signals in two channels simultaneously and reproduces more naturalistic turn taking compared to a text-based cascaded model. |
| Outcome: | The proposed model reproduces more naturalistic and fluid turn taking than a text-based cascaded model. |
XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception (2024.acl-long)
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| Challenge: | Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. |
| Approach: | They propose a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation in over 100 languages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art by 18.5% WER and 4.7 BLEU on downstream audio-visual speech recognition and translation tasks. |
Textless Speech Emotion Conversion using Discrete & Decomposed Representations (2022.emnlp-main)
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Felix Kreuk, Adam Polyak, Jade Copet, Eugene Kharitonov, Tu Anh Nguyen, Morgan Rivière, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
| Challenge: | Existing methods for modifying emotion of speech are difficult because emotion affects all levels simultaneously. |
| Approach: | They propose a method to convert a spoken language speech into a model of emotion . they use phonetic-content units, prosodic features, speaker, and emotion to modify the emotion a speech utterance has. |
| Outcome: | The proposed method beats text-based systems in terms of perceived emotion and audio quality. |
Simple and Effective Unsupervised Speech Translation (2023.acl-long)
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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
| Approach: | They propose a pipeline approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis. |
| Outcome: | The proposed approach outperforms the state-of-the-art in unsupervised speech recognition by 3.2 BLEU on the Libri-Trans benchmark and the best supervised end-to-end models from only two years ago by an average of 5.0 BLUE over five X-En directions. |
On Generative Spoken Language Modeling from Raw Audio (2021.tacl-1)
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Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | Using a set of metrics to evaluate the learned representations, we aim to create a system that learns from natural interactions as infants learn their first language. |
| Approach: | They propose a task of learning acoustic and linguistic characteristics from raw audio and a set of metrics to evaluate the learned representations at acustic, linguistic and encoding levels. |
| Outcome: | The proposed models evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. |
Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)
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Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino
| Challenge: | Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations. |
| Approach: | They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. |
| Outcome: | The proposed method achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task. |
Toward Joint Language Modeling for Speech Units and Text (2023.findings-emnlp)
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Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu, Karen Livescu, Arun Babu, Alexis Conneau, Alexei Baevski, Michael Auli
| Challenge: | Speech and text are two major forms of human language and little effort has been made to model them together. |
| Approach: | They propose to combine speech and text models to create mixed speech-text data by using different tokenizers and automatic metrics to evaluate how well the model mixes speech and texts. |
| Outcome: | The proposed model improves over a speech-only baseline and shows zero-shot cross-modal transferability. |