Abstract :
Alphabet recognition is needed in many applications for retrieving information associated with the spelling of a name, such as telephone, addresses, etc. In this paper Arabic alphabets were investigated from the speech recognition problem point of view. The system was an isolated whole word speech recognizer. Spoken Arabic alphabet has more than one set, each of which contains members of alphabets that are acoustically very similar. This recognition system achieved 83.34% correct for alphabets. The spoken alphabet "Hamzah" got almost 100% recognition rate, but the worst performance was encountered with alphabet "Baa", "Taa", "Thaa", "H_aa", "Thaal", "Raa", "Seen", "T_aa", "Dhaa", and "Faa".
Keywords :
hidden Markov models; natural language processing; speech recognition; HMM; hidden Markov models; information retrieval; speech recognition; spoken Arabic alphabet recognition; Application software; Educational institutions; Hidden Markov models; Information retrieval; Information technology; Mel frequency cepstral coefficient; Natural languages; Signal processing; Speech recognition; Testing; Alphabet; Arabic language; Baa-set; HMM; Speech recognition;
