Conference paper

MATĚJKA Pavel, NOVOTNÝ Ondřej, PLCHOT Oldřich, BURGET Lukáš, DIEZ Sánchez Mireia and ČERNOCKÝ Jan. Analysis of Score Normalization in Multilingual Speaker Recognition. In: Proceedings of Interspeech 2017. Stockholm: International Speech Communication Association, 2017, pp. 1567-1571. ISSN 1990-9772. Available from: http://www.isca-speech.org/archive/Interspeech_2017/pdfs/0803.PDF
Publication language:english
Original title:Analysis of Score Normalization in Multilingual Speaker Recognition
Title (cs):Analýza normalizace skóre v multilingválním rozpoznávání mluvčího
Pages:1567-1571
Proceedings:Proceedings of Interspeech 2017
Conference:Interspeech 2017
Place:Stockholm, SE
Year:2017
URL:http://www.isca-speech.org/archive/Interspeech_2017/pdfs/0803.PDF
Journal:Proceedings of Interspeech, Vol. 2017, No. 08, FR
ISSN:1990-9772
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2017/matejka_interspeech2017_IS170803.pdf [PDF]
Files: 
Keywords
speaker recognition, score normalization
Annotation
This paper is about the analysis of score normalization in multilingual speaker recognition. Several normalization techniques are compared in this paper as well as different cohorts and analyzes the nature of files selected to the cohort in adaptive score normalization.
Abstract
NIST Speaker Recognition Evaluation 2016 has revealed the importance of score normalization for mismatched data conditions. This paper analyzes several score normalization techniques for test conditions with multiple languages. The best performing one for a PLDA classifier is an adaptive s-norm with 30% relative improvement over the system without any score normalization. The analysis shows that the adaptive score normalization (using top scoring files per trial) selects cohorts that in 68% contain recordings from the same language and in 92% of the same gender as the enrollment and test recordings. Our results suggest that the data to select score normalization cohorts should be a pool of several languages and channels and if possible, its subset should contain data from the target domain.
BibTeX:
@INPROCEEDINGS{
   author = {Pavel Mat{\v{e}}jka and Ond{\v{r}}ej Novotn{\'{y}} and
	Old{\v{r}}ich Plchot and Luk{\'{a}}{\v{s}} Burget and Mireia
	S{\'{a}}nchez Diez and Jan {\v{C}}ernock{\'{y}}},
   title = {Analysis of Score Normalization in Multilingual Speaker
	Recognition},
   pages = {1567--1571},
   booktitle = {Proceedings of Interspeech 2017},
   journal = {Proceedings of Interspeech},
   volume = {2017},
   number = {08},
   year = {2017},
   location = {Stockholm, SE},
   publisher = {International Speech Communication Association},
   ISSN = {1990-9772},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en?id=11580}
}

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