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Most current language recognition systems model different levels of information such as acoustic, prosodic, phonotactic, etc. independently and combine the model likelihoods in order to make a decision. However, these are single level systems that treat all languages identically and hence incapable of exploiting any similarities that may exist within groups of languages. In this paper, a hierarchical...
Language identification systems combining i-vectors estimated from different acoustic feature spaces have recently been shown to be superior to i-vector systems based on a single acoustic feature space. Specifically, i-vectors estimated using MFCC and PLP front-ends were concatenated prior to using LDA to obtain a combined i-vector. In this work, we investigate the scalability of this i-vector concatenation...
This paper reports the method of collecting minimally balanced corpus for speech recognition. Generally balanced corpora are used for training speech recognition systems. However, these balanced corpora are not optimal. The current paper demonstrates that these corpora can be reduced to a varied degree for various phonemes for developing a minimally balanced corpus. The experiments have been developed...
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