Multi-level context-dependent acoustic modeling for automatic speech recognition

In this paper, we propose a multi-level, context-dependent acoustic modeling framework for automatic speech recognition. For each context-dependent unit considered by the recognizer, we construct a set of classifiers that target different amounts of contextual resolution, and then combine them for scoring. Since information from multiple levels of contexts is appropriately combined, the proposed modeling framework provides reasonable scores for units with few or no training examples, while maintaining an ability to distinguish between different context-dependent units. On a large vocabulary lecture transcription task, the proposed modeling framework outperforms a traditional clustering-based context-dependent acoustic model by 3.5% (11.4% relative) in terms of word error rate.