Automated Audio Clustering

Voci Technologies Incorporated (Voci) is the leading small business developing accelerated Human Language Technology based solutions. Voci is partnering with Richard M. Stern, a Voci advisor and Professor at Carnegie Mellon University (CMU), to develop an Automated Speaker Clustering System (ASCS). The proposed ASCS will be developed by integrating Voci"s best in class, patent pending, HyperVox technology with the latest SID capabilities from CMU. The proposed system is uniquely architected to provide tuning parameters that enable tradeoffs between false positive and false negative rates, and the ability to simulate the impact of improvements on different components of the ASCS system essential to developing reliable performance specifications. The proposed ASCS uses a parallel set of proprietary techniques to optimize the extraction of voice features in both batch and streaming modes. The resulting voice features are fused with a reliable word list to provide a clustering decision together with a confidence estimate on the match between the audio sample and the nearest speaker cluster. At the end of Phase I the team will demonstrate the automated clustering of audio files. The Team believes its final ASCS implementation will be able to automatically cluster 10"s to 100"s of thousand of audio files per hour with useful true/false positive rates.