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Found 9 papers, 0 papers with code
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.
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
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.
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data.
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection
In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND).
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
