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Found 14 papers, 2 papers with code
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR
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
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.
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization
This paper focuses on speaker diarization and proposes to conduct the above bi-directional knowledge transfer alternately.
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND.
Investigating self-supervised learning for speech enhancement and separation
Speech enhancement and separation are two fundamental tasks for robust speech processing.
Multi-Channel End-to-End Neural Diarization with Distributed Microphones
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.
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
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.
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
Diarization results are then estimated as dot products of the attractors and embeddings.
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge.
Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers
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
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.
Speaker Diarization with Region Proposal Network
Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem.
Speaker detection in the wild: Lessons learned from JSALT 2019
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios.
Audio and Speech Processing Sound
