Compact acoustic model for embedded implementation

An acoustic model for an embedded speech recognition system must exhibit two desirable features; ability to minimize performance degradation in recognition while solving the memory problem under limited system resources. To cope with the challenges, we introduce the state-clustered tied-mixture (SCTM) HMM as an acoustic model optimization. The proposed SCTM modeling shows a significant improvement in recognition performance as well as a solution to sparse training data problem. Moreover, the state weight quantizing method achieves a drastic reduction in model size. In this paper, we describe the acoustic model optimization procedure for embedded speech recognition system and corresponding performance evaluation results.