Hidden Markov Model based isolated Hindi word recognition

In this paper three schemes based on the Hidden Markov Model for recognition of isolated words in Hindi Language speech are discussed; namely speaker dependent, multi speaker and speaker independent. For the study a set of 10 Hindi words is chosen, for which the training followed by testing is performed. The recogniser is built over three basic building blocks namely Feature extraction, Training and Recognition (Testing). The scheme proposed here implements the Mel Frequency Cepstral Coefficients (MFCC) in order to compute the spectral features of the speech signal. Then, K-means algorithm is used to form the codebook by performing clustering over the obtained feature vectors. Recognition of a spoken Hindi word is carried out by first driving its features, and then deciding in favour of the Hindi word whose model likelihood is highest, by implementing the Viterbi algorithm for the given HMM. The recognition rate for speaker dependent isolated word recogniser for total of 10 speakers (7 male, 3 female) is 99% whereas for multi speaker it is 98% (10 male) and for speaker independent (10 male) it is 97.5%. Experiments are carried out to develop a approach towards advancement in this field specifically for Hindi.