A FACTORIAL HMM APPROACH TO ROBUST ISOLATED DIGIT RECOGNITION IN NON-STATIONARY NOISE
This paper presents a novel solution to the problem of isolated digit recognition in non-stationary noise. A Factorial Hidden Markov Model (FHMM) architecture is proposed that accurately models the simultaneous occurrence of two independent processes, i.e. an utterance of a digit and a clip of non-stationary noise. The FHMM is implemented with its equivalent HMM by extending Nadas’ MIXMAX algorithm to a mixture of Gaussians PDF. The proposed system is tested on the recognition of two digits spoken simultaneously by different talkers as well as on the recognition of isolated digits mixed with background music. At 0 dB SNR, the simultaneous recognition accuracy is improved by 105% over baseline. The system also shows a relative improvement of up to 170% over baseline in the recognition of isolated digits immersed in music at 0 dB SNR.