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Found 2 papers, 1 papers with code
A Light Weight Model for Active Speaker Detection
Experimental results on the AVA-ActiveSpeaker dataset show that our framework achieves competitive mAP performance (94. 1% vs. 94. 2%), while the resource costs are significantly lower than the state-of-the-art method, especially in model parameters (1. 0M vs. 22. 5M, about 23x) and FLOPs (0. 6G vs. 2. 6G, about 4x).
Audio-Visual Active Speaker Detection
speaker-diarization
+2
Monolingual Recognizers Fusion for Code-switching Speech Recognition
It has two stages: the speech awareness (SA) stage and the language fusion (LF) stage.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
