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Found 6 papers, 0 papers with code
Multi-channel Conversational Speaker Separation via Neural Diarization
To enhance ASR performance in conversational or meeting environments, continuous speaker separation (CSS) is commonly employed.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-resolution location-based training for multi-channel continuous speech separation
The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation
This prevents the PSE model from being too aggressive while still allowing the model to learn to suppress the input speech when it is likely to be spoken by interfering speakers.
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Location-based training for multi-channel talker-independent speaker separation
We further demonstrate the effectiveness of LBT for the separation of four and five concurrent speakers.
End-to-end attention-based distant speech recognition with Highway LSTM
End-to-end attention-based models have been shown to be competitive alternatives to conventional DNN-HMM models in the Speech Recognition Systems.
