Context-dependent modeling in alphabet recognition

Alphabet recognition is known to be a difficult task due to the acoustic similarities among different letters, especially letters in the E-set. Recognition systems based on whole-word Hidden Markov Models (HMM) perform poorly on this task due to the inability of the models to capture fine phonetic details, especially details occurring within segments of short duration. Letters B and D, for example, differ mainly in the 10-20 msec segment prior to vowel onset. In this paper, we use context-dependent phoneme-based HMMs to capture the fine phonetic detail that is required to discriminate such a confusable vocabulary. Our results reveal that context-dependent modeling gives about 9% improvement on speaker-independent performance over whole-word modeling, and an 18% improvement on the E-set. Furthermore, using an improved spectral representation of the stop consonants in the E-set, an additional 6% improvement in the E-set can be achieved. Our best speaker-independent E-set performance over 15 speakers is 90.3%, with overall alphabet recognition of 94.1%