Speaker Linking and Applications using Non-Parametric Hashing Methods
Abstract
Large unstructured audio data sets have become ubiquitous and present a challenge for organization and search. One logical approach for structuring data is to find common speakers and link occurrences across different recordings. Prior approaches to this problem have focused on basic methodology for the linking task. In this paper, we introduce a novel trainable nonparametric hashing method for indexing large speaker recording data sets. This approach leads to tunable computational complexity methods for speaker linking. We focus on a scalable clustering method based on hashingcanopy-clustering. We apply this method to a large corpus of speaker recordings, demonstrate performance tradeoffs, and compare to other hashing methods.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 08, 2016
- Accession Number
- AD1033823
Entities
People
- Douglas E. Sturim
- William M. Campbell
Organizations
- MIT Lincoln Laboratory