Fully Unsupervised Topic Clustering of Unlabelled Spoken Audio Using Self-Supervised Representation Learning and Topic Model-Reference-Cited by-同舟云学术

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Fully Unsupervised Topic Clustering of Unlabelled Spoken Audio Using Self-Supervised Representation Learning and Topic Model

Published:2023-06-04 Issue: Volume: Page:
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Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Maekaku Takashi¹,Fujita Yuya¹,Chang Xuankai²,Watanabe Shinji²

Affiliation:

1. Yahoo Japan Corporation,Tokyo,Japan

2. Carnegie Mellon University,PA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095280.pdf?arnumber=10095280

Reference39 articles.

1. Unsupervised audio patterns discovery using HMM-based self-organized units

2. GigaSpeech: An Evolving, Multi-Domain ASR Corpus with 10,000 Hours of Transcribed Audio

3. Zero resource spoken audio corpus analysis

4. Libri-Light: A Benchmark for ASR with Limited or No Supervision

5. HuBERT: How much can a bad teacher benefit ASR pre-training;hsu;Workshop on Self-Supervised Learning for Speech and Audio Processing NeurIPS,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hubertopic: Enhancing Semantic Representation of Hubert Through Self-Supervision Utilizing Topic Model;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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