NDLI: Fast speaker diarization using a high-level scripting language

Content Provider	IEEE Xplore Digital Library
Author	Gonina, E. Friedland, G. Cook, H. Keutzer, K.
Copyright Year	2011
Description	Author affiliation: University of California, Berkeley, USA (Gonina, E.; Cook, H.; Keutzer, K.) \|\| International Computer Science Institute, USA (Friedland, G.)
Abstract	Most current speaker diarization systems use agglomerative clustering of Gaussian Mixture Models (GMMs) to determine “who spoke when” in an audio recording. While state-of-the-art in accuracy, this method is computationally costly, mostly due to the GMM training, and thus limits the performance of current approaches to be roughly real-time. Increased sizes of current datasets require processing of hundreds of hours of data and thus make more efficient processing methods highly desirable. With the emergence of highly parallel multicore and manycore processors, such as graphics processing units (GPUs), one can re-implement GMM training to achieve faster than real-time performance by taking advantage of parallelism in the training computation. However, developing and maintaining the complex low-level GPU code is difficult and requires a deep understanding of the hardware architecture of the parallel processor. Furthermore, such low-level implementations are not readily reusable in other applications and not portable to other platforms, limiting programmer productivity. In this paper we present a speaker diarization system captured in under 50 lines of Python that achieves 50–250× faster than real-time performance by using a specialization framework to automatically map and execute computationally intensive GMM training on an NVIDIA GPU, without significant loss in accuracy.
Starting Page	553
Ending Page	558
File Size	307095
Page Count	6
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163887
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics processing unit Training Instruction sets Covariance matrix Kernel Real time systems Hardware
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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