We lifted 6 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| BUTSpeechFIT/VBx | canonical | 3 of 3 |
| hltcoe/VBx | extension | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| DER | Ran | BUTSpeechFIT/VBx/VBx/VBx.py pointer only (licence: NONE) · get_code("1f5f24ae84241edc") |
| VBx | Ran | BUTSpeechFIT/VBx/VBx/VBx.py pointer only (licence: NONE) · get_code("09ed78b77685499e") |
| forward_backward | Ran | BUTSpeechFIT/VBx/VBx/VBx.py pointer only (licence: NONE) · get_code("259e6f64ed883164") |
| frame_labels2posterior_mx | Ran | hltcoe/VBx/VBx/VB_diarization.py pointer only (licence: NONE) · get_code("b331ed6ad2fffb96") |
| VB_diarization | Not yet run | hltcoe/VBx/VBx/VB_diarization.py pointer only (licence: NONE) · get_code("6ea546d219816cd8") |
| precalculate_VtiEV | Not yet run | hltcoe/VBx/VBx/VB_diarization.py pointer only (licence: NONE) · get_code("8948ed6f7ec7197a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The recently proposed VBx diarization method uses a Bayesian hidden Markov model to find speaker clusters in a sequence of x-vectors. In this work we perform an extensive comparison of performance of the VBx diarization with other approaches in the literature and we show that VBx achieves superior performance on three of the most popular datasets for evaluating diarization: CALLHOME, AMI and DIHARDII datasets. Further, we present for the first time the derivation and update formulae for the VBx model, focusing on the efficiency and simplicity of this model as compared to the previous and more complex BHMM model working on frame-by-frame standard Cepstral features. Together with this publication, we release the recipe for training the x-vector extractors used in our experiments on both wide and narrowband data, and the VBx recipes that attain state-of-the-art performance on all three datasets. Besides, we point out the lack of a standardized evaluation protocol for AMI dataset and we propose a new protocol for both Beamformed and Mix-Headset audios based on the official AMI partitions and transcriptions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2012.14952")
get_code_for_paper("2012.14952")
have("2012.14952")
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