Search Results for author:
Found 10 papers, 4 papers with code
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
Online End-to-End Neural Diarization with Speaker-Tracing Buffer
This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND).
Neural Speaker Diarization with Speaker-Wise Chain Rule
Speaker diarization is an essential step for processing multi-speaker audio.
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors
End-to-end speaker diarization for an unknown number of speakers is addressed in this paper.
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
However, the clustering-based approach has a number of problems; i. e., (i) it is not optimized to minimize diarization errors directly, (ii) it cannot handle speaker overlaps correctly, and (iii) it has trouble adapting their speaker embedding models to real audio recordings with speaker overlaps.
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models
Our proposed method combined with i-vector speaker embeddings ultimately achieved a WER that differed by only 2. 1 % from that of TS-ASR given oracle speaker embeddings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
End-to-End Neural Speaker Diarization with Self-attention
Our method was even better than that of the state-of-the-art x-vector clustering-based method.
Ranked #2 on
Speaker Diarization
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