High-performance alphabet recognition

P. C. Loizou, Andreas S. Spanias

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

Alphabet recognition is needed in many applications for retrieving information associated with the spelling of a name, such as telephone numbers, addresses, etc. This is a difficult recognition task due to the acoustic similarities existing between letters in the alphabet (e.g., the E-set letters). This paper presents the development of a high-performance alphabet recognizer that has been evaluated on studio quality as well as on telephone-bandwidth speech. Unlike previously proposed systems, the alphabet recognizer presented here is based on context-dependent phoneme hidden Markov models (HMM's), which have been found to outperform whole-word models by as much as 8%. The proposed recognizer incorporates a series of new approaches to tackle the problems associated with the confusions occurring between the stop consonants in the E-set and the confusions between the nasals (i.e., letters M and N). First, a new feature representation is proposed for improved stop consonant discrimination, and second, two subspace approaches are proposed for improved nasal discrimination. The subspace approach was found to yield a 45% error-rate reduction in nasal discrimination. Various other techniques are also proposed, yielding a 97.3% speaker-independent performance on alphabet recognition and 95% speaker-independent performance on E-set recognition. A telephone alphabet recognizer was also developed using context-dependent HMM's. When tested on the recognition of 300 last names (which are contained in a list of 50 000 common last names) spelled by 300 speakers, the recognizer achieved 91.7% correct letter recognition with 1.1% letter insertions.

Original languageEnglish (US)
Pages (from-to)430-445
Number of pages16
JournalIEEE Transactions on Speech and Audio Processing
Volume4
Issue number6
DOIs
StatePublished - Dec 1 1996
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Acoustics and Ultrasonics
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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