Speaker Diarization
Speaker diarization is the task of identifying "who spoke when" in an audio recording, a crucial preprocessing step for many speech applications. Current research focuses on improving accuracy and efficiency, particularly in challenging scenarios like multi-speaker conversations and noisy environments, using techniques such as end-to-end neural networks, spectral clustering, and the integration of audio-visual or semantic information. These advancements are driving progress in areas like meeting transcription, multilingual speech processing, and improving the performance of downstream tasks such as automatic speech recognition.
Papers
Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network
Yiling Huang, Weiran Wang, Guanlong Zhao, Hank Liao, Wei Xia, Quan Wang
DiaCorrect: Error Correction Back-end For Speaker Diarization
Jiangyu Han, Federico Landini, Johan Rohdin, Mireia Diez, Lukas Burget, Yuhang Cao, Heng Lu, Jan Cernocky