GMM is one of the most successful models in the field of automatic language identification. In this paper we have proposed a new model named adapted weight GMM (AW-GMM). This model is similar to GMM but the weights are determined using GMM-VSM LID system based on the po More
GMM is one of the most successful models in the field of automatic language identification. In this paper we have proposed a new model named adapted weight GMM (AW-GMM). This model is similar to GMM but the weights are determined using GMM-VSM LID system based on the power of each component in discriminating one language from the others. Also considering the computational complexity of GMM-VSM, we have proposed a technique for constructing bigram sequences of components which could be used for higher sequence orders and decreases the complexity. Experiments on four languages of OGI corpus including English, Farsi, French and German have shown the effectiveness of proposed techniques.
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The GMM model is one of the most widely used and successful models in the field of automatic language recognition. In this article, a new model called Adapted Weight-GMM (AW-GMM) is presented. This model is similar to GMM, with the difference that the weight of its comp More
The GMM model is one of the most widely used and successful models in the field of automatic language recognition. In this article, a new model called Adapted Weight-GMM (AW-GMM) is presented. This model is similar to GMM, with the difference that the weight of its components in the form of GMM-VSM system is determined based on the strength of the components in differentiating one language from other languages. Also, due to the computational complexity in the GMM-VSM system in the case where a 2-component sequence is considered, a technique for constructing a 2-component sequence has been presented, which can be used to construct higher-order sequences as well. used The evaluations carried out on 4 languages English, Persian, French and German from OGI data show the effectiveness of the presented techniques.
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