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Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition

机译:用于语言和方言识别的高斯混合模型权重自适应的非负因素分析

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摘要

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.
机译:最近的研究表明,高斯混合模型(GMM)权重对于GMM手段携带的语言和方言识别信息较少,但却是互补的。但是,最新的语言识别系统通常不使用此信息。在这项研究中,开发了一种用于GMM权重分解和自适应的非负因素分析(NFA)方法。这种在概念上简单且计算上不昂贵的建模建议了一种使用类似于i-vector框架的因子分析的新的低维话语表示方法。然后,将获得的子空间向量与i向量一起应用于语言/方言识别问题。 NIST 2011和RATS语言识别评估(LRE)语料库以及QCRI阿拉伯方言识别评估(DRE)语料库对建议的方法进行了评估。评估结果表明,与三种传统的权重自适应方法(即最大似然重新估计,非负矩阵分解和子空间多项式模型)相比,所提出的自适应方法可获得更准确的识别结果。实验结果还表明,i-向量和NFA子空间向量的中间级融合提高了最新i-向量框架的性能,特别是在短话语的情况下。

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