Automatic speech recognition of Urdu words using linear discriminant analysis

Urdu is amongst the five largest languages of the world and possess a very important role as it shares its vocabulary with languages as Arabic, Persian, Hindi and several other languages of the Indo-Pak. The Automatic Speech Recognition task of Urdu has not been addressed significantly. This paper presents the statistical based classification technique to achieve the task of Automatic Speech Recognition of isolated words in Urdu. The proposed approach is based on calculation of 52 Mel Frequency Cepstral Coefficients for each isolated word. The classification has been achieved with Linear Discriminant Analysis. The successful or incorrect matches have been presented in the Confusion Matrix. As a prototype, the framework has been trained with audio samples of seven speakers including male/female, native/non-native and speakers with different ages. The test set comprises of audio data of three speaker. For each isolated, percentage error has been calculated. It was found that majority of the words are recognized with percentage error less than 33%. Some words suffer 100% error and were referred to be the bad words. This work may provide a baseline for further research on Urdu Automatic Speech Recognition.