SCORE NORMALIZATION AND SYSTEM COMBINATION FOR IMPROVED KEYWORD SPOTTING

被引：0

作者：

Karakos, Damianos ^{[1
]}

Schwartz, Richard ^{[1
]}

Tsakalidis, Stavros ^{[1
]}

Zhang, Le ^{[1
]}

Ranjan, Shivesh ^{[1
]}

Ng, Tim ^{[1
]}

Hsiao, Roger ^{[1
]}

Saikumar, Guruprasad ^{[1
]}

Bulyko, Ivan ^{[1
]}

Long Nguyen ^{[1
]}

Makhoul, John ^{[1
]}

Grezl, Frantisek ^{[2
]}

Hannemann, Mirko ^{[2
]}

Karafiat, Martin ^{[2
]}

Szoke, Igor ^{[2
]}

Vesely, Karel ^{[2
]}

Lamel, Lori ^{[3
]}

Le, Viet-Bac ^{[4
]}

机构：

[1] Raytheon BBN Technol, Cambridge, MA 02138 USA

[2] Brno Univ Technol, SpeechFIT, Brno, Czech Republic

[3] CNRS LIMSI, Paris, France

[4] Vocapia Res, Paris, France

来源：

2013 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING (ASRU) | 2013年

关键词：

keyword search; score normalization; system combination; indexing and search;

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：

We present two techniques that are shown to yield improved Keyword Spotting (KWS) performance when using the ATWV/MTWV performance measures: (i) score normalization, where the scores of different keywords become commensurate with each other and they more closely correspond to the probability of being correct than raw posteriors; and (ii) system combination, where the detections of multiple systems are merged together, and their scores are interpolated with weights which are optimized using MTWV as the maximization criterion. Both score normalization and system combination approaches show that significant gains in ATWV/MTWV can be obtained, sometimes on the order of 8-10 points (absolute), in five different languages. A variant of these methods resulted in the highest performance for the official surprise language evaluation for the IARPA-funded Babel project in April 2013.

引用

页码：210 / 215

页数：6

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