Bi-LSTM Scoring Based Similarity Measurement with Agglomerative Hierarchical Clustering (AHC) for Speaker Diarization. (arXiv:2205.09709v1 [eess.AS])

Majority of speech signals across different scenarios are never available
with well-defined audio segments containing only a single speaker. A typical
conversation between two speakers consists of segments where their voices
overlap, interrupt each other or halt their speech in between multiple
sentences. Recent advancements in diarization technology leverage neural
network-based approaches to improvise multiple subsystems of speaker
diarization system comprising of extracting segment-wise embedding features and
detecting changes in the speaker during conversation. However, to identify
speaker through clustering, …

arxiv bi bi-lstm clustering hierarchical lstm measurement scoring