Online Unsupervised Pattern Discovery in Speech using Parallelization

被引：0
|
作者：
Gajjar, Mrugesh R. [1 ]
Govindarajan, R. [1 ]
Sreenivas, T. V. [2 ]
机构：
[1] Indian Inst Sci, Supercomp Educ & Res Ctr, Bangalore 560012, Karnataka, India
[2] Indian Inst Sci, Dept Elect Commun Engn, Bangalore 560012, Karnataka, India
来源：
INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5 | 2008年
关键词：
Unsupervised pattern discovery; Dynamic time warping; Parallelization; Spoken language systems;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Segmental dynamic time warping (DTW) has been demonstrated to be a useful technique for finding acoustic similarity scores between segments of two speech utterances. Due to its high computational requirements, it had to be computed in an offline manner, limiting the applications of the technique. In this paper, we present results of parallelization of this task by distributing the workload in either a static or dynamic way on an 8-processor cluster and discuss the trade-offs among different distribution schemes. We show that online unsupervised pattern discovery using segmental DTW is plausible with as low as 8 processors. This brings the task within reach of today's general purpose multi-core servers. We also show results on a 32-processor system, and discuss factors affecting scalability of our methods.
引用
收藏
页码：2458 / +
页数：2
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