Speech Recognition System of Arabic Digits based on A Telephony Arabic Corpus

Automatic recognition of spoken digits is one of the difficult tasks in the field of computer speech recognition. Spoken digits recognition process is required in many applications such as speech based telephone dialing, airline reservation, automatic directory to retrieve or send information, etc. These applications take numbers and alphabets as input. Arabic language is a Semitic language that differs from European languages such as English. One of these differences is how to pronounce the ten digits, zero through nine. In this research, spoken Arabic digits are investigated from the speech recognition problem point of view. The system is designed to recognize an isolated whole-word speech. The Hidden Markov Model Toolkit (HTK) is used to implement the isolated word recognizer with phoneme based HMM models. In the training and testing phase of this system, isolated digits data sets are taken from the telephony Arabic speech corpus, SAAVB. This standard corpus was developed by KACST and it is classified as a noisy speech database. A hidden Markov model based speech recognition system was designed and tested with automatic Arabic digits recognition. This recognition system achieved 93.72% overall correct rate of digit recognition