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This paper proposes an acoustic modeling approach based on bootstrap and restructuring to dealing with data sparsity for low-resourced languages. The goal of the approach is to improve the statistical reliability of acoustic modeling for automatic speech recognition (ASR) in the context of speed, memory and response latency requirements for real-world applications. In this approach, randomized hidden...
In this paper, we propose a novel technique of using cross validation (CV) data sampling to construct an ensemble of acoustic models for conversational speech recognition. We further propose using hierarchical Gaussian mixture model (HGMM) and repartition training data to increase the ensemble size and diversity. The proposed methods are found to work well together for ensemble acoustic modeling....
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