speakerdiarization

Speaker diarization is the process of partitioning an audio stream into segments according to the speaker who is talking. It addresses the question "who spoke when?". This is distinct from speaker recognition, which identifies a specific speaker, or speech recognition, which transcribes spoken words. Diarization aims to create a timeline of speech segments, assigning each segment to a distinct speaker label.

The core challenge in speaker diarization lies in distinguishing between different speakers based on their vocal characteristics. This often involves acoustic modeling to capture features like pitch, timbre, and speaking rate. Common techniques include clustering algorithms applied to speaker embeddings extracted from the audio. These embeddings represent the unique acoustic properties of each speaker.

Speaker diarization has numerous applications. In call center analytics, it can help identify which agent and customer spoke during a conversation, facilitating performance analysis and quality control. For media transcription, it enables the automatic labeling of different speakers in interviews or multi-person discussions. In assistive technologies, it can improve the experience for individuals with hearing impairments by clearly indicating who is speaking. Research continues to focus on improving robustness to noisy environments, overlapping speech, and a large number of speakers.