2018/11/27 by Suwon Shon, Shon, Suwon, Younggun Lee +3
Computer Science · Engineering · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Information Retrieval (cs.IR) #Music and Audio Processing #Speech Recognition and Synthesis #Speech and Audio Processing #cs.IR #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1811.10812
Interspeech 2019
openalex publication_date 2018/11/27 · arxiv created 2019/06/18 · arxiv updated 2019/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper describes a fast speaker search system to retrieve segments of the same voice identity in the large-scale data. A recent study shows that Locality Sensitive Hashing (LSH) enables quick retrieval of a relevant voice in the large-scale data in conjunction with i-vector while maintaining accuracy. In this paper, we proposed Random Speaker-variability Subspace (RSS) projection to map a data into LSH based hash tables. We hypothesized that rather than projecting on completely random subspace without considering data, projecting on randomly generated speaker variability space would give more chance to put the same speaker representation into the same hash bins, so we can use less number of hash tables. Multiple RSS can be generated by randomly selecting a subset of speakers from a large speaker cohort. From the experimental result, the proposed approach shows 100 times and 7 times faster than the linear search and LSH, respectively