In this paper, this technique is generalized by using Gaussian mixture models as the basis for tokenizing. Performance results are presented for a system ...
In this paper, this technique is generalized by using Gaussian mixture models as the basis for tokenizing. Performance results are presented for a system ...
Phone tokenization followed by n-gram language modeling has consistently provided good results for the task of language identification, but this technique ...
Phone tokenization followed by n-gram language modeling has consistently provided good results for the task of language identification.
The approaches include both acoustic scoring and a recently developed GMM tokenization system that is based on a variation of phonetic recognition and language ...
The statistical model of Gaussian Mixture Models. (GMMs) were chosen for this research due to their ability to represent an entire language with a single ...
This paper describes two GMM-based approaches to language identi- fication that use shifted delta cepstra (SDC) feature vectors to achieve LID performance.
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In this paper, this technique is generalized by using Gaussian mixture models as the basis for tokenizing. Performance results are presented for a system ...
It is proved that GMM tokenization with language modeling achieves minimal error rate and efficient identification performance. In the literature it found that ...
Two GMM-based approaches to language identification that use shifted delta cepstra (SDC) feature vectors to achieve LID performance comparable to that of ...