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A diagram of feature extraction and speaker recognition framework is shown in Figure 1. Figure 1 Feature extraction and recognition framework.
This recognition framework puts the highest demand on the quality of the acoustic models.
On these bases, our goal is to build a recognition framework based on human cognition theory.
AM designed the agglomerate recognition framework, performed the experiments, and drafted the manuscript.
Huynh [24] combined a generative model (multiple eigenspace) with SVM classifier into their activity recognition framework.
This paper introduces a novel recognition framework for human actions using hybrid features.
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In future work, we will first attempt to improve the discriminative power of the multiple classification framework and investigate how feature representation errors impact recognition frameworks.
The results of both methodologies have demonstrated promising outcomes as far as discovery; they are great possibility to constitute a collaboration to expand or prove recognition frameworks as far as runtime speed and grouping exactness.
In this paper, we have presented an automated identification framework for the recognition of classical music composers using a novel n-gram based feature extraction technique along with a cortical algorithm for the feature reduction and recognition stages.
A further example is the reverberation modeling for speech recognition (REMOS) framework [8], which combines a clean speech model with a reverberation model to determine clean speech and reverberation estimates during recognition via a modified Viterbi algorithm.
To overcome this weakness of the MTR method, a multiple-model based speech recognition (MMSR) framework was recently proposed, and successful results using this approach were demonstrated [8].
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