Conversational Short Phrase Speaker Diarization

Conversational short phrase speaker diarization (CSSD) aims to accurately identify who spoke when in conversations, particularly focusing on short utterances crucial for semantic understanding. Current research emphasizes improving accuracy using neural network architectures, including sequence-to-sequence models and detection-based methods, often incorporating multi-speaker embeddings and attention mechanisms to handle overlapping speech. This area is vital for advancing speech processing technologies, enabling improved performance in downstream tasks such as speech recognition and natural language processing, particularly in conversational AI applications. The development of new evaluation metrics, like conversational diarization error rate (CDER), better reflects the challenges posed by short phrases in conversational speech.