Abstract:Acoustic models based on Gaussian mixture models (GMMs) typically use short span acoustic feature inputs. This does not capture long-term temporal information from speech owing to the conditional independence assumption of hidden Markov models. In this paper, we present an implicit approach that approximates the joint distribution of long span features by product of factorized models, in contrast to deep neural networks (DNNs) that model feature correlations directly. The approach is applicable to a broad rang… Show more
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