ACID/HNN: clustering hierarchies of neural networks for context-dependent connectionist acoustic modeling

We present the ACID/HNN framework, a principled approach to hierarchical connectionist acoustic modeling in large vocabulary conversational speech recognition (LVCSR). Our approach consists of an agglomerative clustering algorithm based on information divergence (ACID) to automatically design and robustly estimate hierarchies of neural networks (HNN) for arbitrarily large sets of context-dependent decision tree clustered HMM states. We argue that a hierarchical approach is crucial in applying locally discriminative connectionist models to the typically very large state spaces observed in LVCSR systems. We evaluate the ACID/HNN framework on the Switchboard conversational telephone speech corpus. Furthermore, we focus on the benefits of the proposed connectionist acoustic model, namely exploiting the hierarchical structure for speaker adaptation and decoding speed-up algorithms.