Článek v časopise

BAHARI Mohamad H., DEHAK Najim, VAN hamme Hugo, BURGET Lukáš, ALI Ahmed M. a GLASS Jim. Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition. IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING. New York City: IEEE Signal Processing Society, 2014, roč. 2014, č. 7, s. 1117-1129. ISSN 2329-9290. Dostupné z: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6803908
Jazyk publikace:angličtina
Název publikace:Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition
Název (cs):Non-negativní faktorová analýza adaptace modelu se směsí Gaussovek pro rozpoznávání jazyka a dialektu
Strany:1117-1129
Místo vydání:US
Rok:2014
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6803908
Časopis:IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING, roč. 2014, č. 7, New York City, US
ISSN:2329-9290
DOI:10.1109/TASLP.2014.2319159
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2014/bahari_acm_trans2014_06803908.pdf [PDF]
Klíčová slova
Non-negative factor analysis; model adaptation; Gaussian mixture model weight; dialect recognition; language recognition
Anotace
Práce popisuje metodu pro dekompozici a adaptaci vah modelu směsice gaussovských rozložení. Tato metoda dovoluje reprezentovat promluvy pomocí nízkodimenzionálních vektoů podobných i-vektorům.
Abstrakt
Recent studies show that Gaussian mixture model (GMM) weights carry less, yet complimentary, information to GMM means for language and dialect recognition. However, state-of-the-art language recognition systems usually do not use this information. In this research, a non-negative factor analysis (NFA) approach is developed for GMM weight decomposition and adaptation. This modeling, which is conceptually simple and computationally inexpensive, suggests a new low-dimensional utterance representation method using a factor analysis similar to that of the i-vector framework. The obtained subspace vectors are then applied in conjunction with i-vectors to the language/dialect recognition problem. The suggested approach is evaluated on the NIST 2011 and RATS language recognition evaluation (LRE) corpora and on the QCRI Arabic dialect recognition evaluation (DRE) corpus. The assessment results show that the proposed adaptation method yields more accurate recognition results compared to three conventional weight adaptation approaches, namely maximum likelihood re-estimation, non-negative matrix factorization, and a subspace multinomial model. Experimental results also show that the intermediate-level fusion of i-vectors and NFA subspace vectors improves the performance of the state-of-the-art i-vector framework especially for the case of short utterances.
BibTeX:
@ARTICLE{
   author = {H. Mohamad Bahari and Najim Dehak and Hugo hamme
	Van and Luk{\'{a}}{\v{s}} Burget and M. Ahmed Ali
	and Jim Glass},
   title = {Non-Negative Factor Analysis of Gaussian Mixture
	Model Weight Adaptation for Language and Dialect
	Recognition},
   pages = {1117--1129},
   journal = {IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE
	PROCESSING},
   volume = {2014},
   number = {7},
   year = {2014},
   ISSN = {2329-9290},
   doi = {10.1109/TASLP.2014.2319159},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.cs?id=10731}
}

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