Discriminatively Trained Context-Dependent Duration-Bigram Models for Korean Digit Recognition

The recognition of continuously spoken Korean digits is well known to be a particularly challenging task among small vocabulary recognition problems. In this paper, we review and evaluate our acoustic modeling efforts for the purpose of efficient high-accuracy recognition of Korean digit strings for in-car applications. The measures comprise context-dependent word models, duration-dependent distribution functions, error-weighted discriminative training as well as a compressed bigram model that strongly constrains the HMM state durations. Finally, an average word error rate across multiple channel and noise conditions of below 3% is achieved, which is a relative reduction of 62% over the error observed with traditional context-independent digit modeling techniques and about 36% relative error reduction compared to ML-trained context-dependent digit models of ordinary linear topology. Fast unsupervised model adaptation during decoding yields additional 10% of relative improvement