Context-dependent modeling in alphabet recognition

Philipos C. Loizou, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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%.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Number of pages4
StatePublished - 1994
EventProceedings of the 1994 IEEE International Symposium on Circuits and Systems. Part 3 (of 6) - London, England
Duration: May 30 1994Jun 2 1994


OtherProceedings of the 1994 IEEE International Symposium on Circuits and Systems. Part 3 (of 6)
CityLondon, England

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials


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