A Real-time Speaker Diarization System Based on Spatial Spectrum
This work provides a solution for real-time speaker identification and tracking in meetings or conversations, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of real-time speaker diarization in conversations by addressing challenges like overlapping speech, dynamic speaker counts, and speaker tracking, achieving significant gains through spatial information integration.
In this paper we describe a speaker diarization system that enables localization and identification of all speakers present in a conversation or meeting. We propose a novel systematic approach to tackle several long-standing challenges in speaker diarization tasks: (1) to segment and separate overlapping speech from two speakers; (2) to estimate the number of speakers when participants may enter or leave the conversation at any time; (3) to provide accurate speaker identification on short text-independent utterances; (4) to track down speakers movement during the conversation; (5) to detect speaker change incidence real-time. First, a differential directional microphone array-based approach is exploited to capture the target speakers' voice in far-field adverse environment. Second, an online speaker-location joint clustering approach is proposed to keep track of speaker location. Third, an instant speaker number detector is developed to trigger the mechanism that separates overlapped speech. The results suggest that our system effectively incorporates spatial information and achieves significant gains.