Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks
Results and benchmarks
Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks presents a hidden markov model approach for speaker diarisation.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
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- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: X-Vectors: Robust DNN Embeddings for Speaker Recognition.
- Start from this likely method family: Bayesian probability.
Time to first repro: a few days
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Reproduction readiness
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Hardware requirements
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Validation caveat
Hugging Face artifacts
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Research context
15
Citations
69
References
Tasks
Speaker diarisation, Computer science, Cluster analysis, Frame (networking), Headset, Narrowband, Pattern recognition (psychology), Speaker recognition
Methods
Hidden Markov model, Bayesian probability
Domains
Speech recognition, Artificial intelligence
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