UniTalk is a larger and more diverse active speaker detection benchmark on which state-of-the-art models underperform, and it improves cross-dataset generalization when used as a training source.
LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization
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abstract
More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction. Still, in the clustering stage, traditional algorithms like probabilistic linear discriminant analysis (PLDA) are widely used for scoring the similarity between two speech segments. In this paper, we propose a supervised method to measure the similarity matrix between all segments of an audio recording with sequential bidirectional long short-term memory networks (Bi-LSTM). Spectral clustering is applied on top of the similarity matrix to further improve the performance. Experimental results show that our system significantly outperforms the state-of-the-art methods and achieves a diarization error rate of 6.63% on the NIST SRE 2000 CALLHOME database.
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Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness
UniTalk is a larger and more diverse active speaker detection benchmark on which state-of-the-art models underperform, and it improves cross-dataset generalization when used as a training source.