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- W2552023628 abstract "In this paper, an attempt is made to examine and evaluate the effect of bottleneck and the hierarchical bottleneck (HBN) framework in MLP-based Automatic Speech Recognition (ASR) systems. In particular, the bottleneck and hierarchical bottleneck framework are analyzed using Volterra series. Experiments on several architectures with incorporation of systematic hierarchical and bottleneck properties are done. We obtain significant increase in % Phone Recognition Accuracy (PRA) as compared to traditional cepstral features based Hidden Markov Model (HMM) acoustic modeling to more complex architectures such as the HBN framework. To this extent, the best results are achieved when tandem and acoustic features are combined in a deep HBN framework with 73.72 % PRA on entire TIMIT database. In addition, we observe a relative drop in dependence of language model (LM) on final % PRA with this proposed architecture." @default.
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- W2552023628 date "2016-06-01" @default.
- W2552023628 modified "2023-09-27" @default.
- W2552023628 title "Analysis of hierarchical bottleneck framework for improved phoneme recognition" @default.
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- W2552023628 doi "https://doi.org/10.1109/spcom.2016.7746673" @default.
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