Search Results for author: Paola Garcia

Found 14 papers, 2 papers with code

Bridging Speech and Textual Pre-trained Models with Unsupervised ASR

no code implementations6 Nov 2022 Jiatong Shi, Chan-Jan Hsu, Holam Chung, Dongji Gao, Paola Garcia, Shinji Watanabe, Ann Lee, Hung-Yi Lee

To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

On Compressing Sequences for Self-Supervised Speech Models

no code implementations13 Oct 2022 Yen Meng, Hsuan-Jui Chen, Jiatong Shi, Shinji Watanabe, Paola Garcia, Hung-Yi Lee, Hao Tang

Subsampling while training self-supervised models not only improves the overall performance on downstream tasks under certain frame rates, but also brings significant speed-up in inference.

Self-Supervised Learning

Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization

no code implementations7 Oct 2022 Shota Horiguchi, Yuki Takashima, Shinji Watanabe, Paola Garcia

This paper focuses on speaker diarization and proposes to conduct the above bi-directional knowledge transfer alternately.

Knowledge Distillation speaker-diarization +2

Multi-Channel End-to-End Neural Diarization with Distributed Microphones

no code implementations10 Oct 2021 Shota Horiguchi, Yuki Takashima, Paola Garcia, Shinji Watanabe, Yohei Kawaguchi

With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel input was given while maintaining comparable performance with a single-channel input.

speaker-diarization Speaker Diarization

Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors

no code implementations4 Jul 2021 Shota Horiguchi, Shinji Watanabe, Paola Garcia, Yawen Xue, Yuki Takashima, Yohei Kawaguchi

This makes it possible to produce diarization results of a large number of speakers for the whole recording even if the number of output speakers for each subsequence is limited.


Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers

no code implementations21 Jan 2021 Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Paola Garcia, Kenji Nagamatsu

We propose a streaming diarization method based on an end-to-end neural diarization (EEND) model, which handles flexible numbers of speakers and overlapping speech.

Speaker Diarization Sound Audio and Speech Processing

End-to-End Speaker Diarization as Post-Processing

no code implementations18 Dec 2020 Shota Horiguchi, Paola Garcia, Yusuke Fujita, Shinji Watanabe, Kenji Nagamatsu

Clustering-based diarization methods partition frames into clusters of the number of speakers; thus, they typically cannot handle overlapping speech because each frame is assigned to one speaker.

Clustering Multi-Label Classification +2

Speaker Diarization with Region Proposal Network

1 code implementation14 Feb 2020 Zili Huang, Shinji Watanabe, Yusuke Fujita, Paola Garcia, Yiwen Shao, Daniel Povey, Sanjeev Khudanpur

Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem.

Region Proposal speaker-diarization +1

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