Hybrid Hidden Markov Model and Artificial Neural Network for Automatic Speech Recognition

Automatic speech recognition (ASR) is an important topic to be performed by a computer system. This paper presents the use of a hybrid Hidden Markov Model (HMM) and Artificial Neural Networks (ANNs) for automatic speech recognition. The proposed hybrid system for ASR is to take advantage from the properties of both HMM and ANN, improving flexibility and recognition performance. The hybrid ANN/HMM assumes that the output of an ANN is sent to the HMM for ASR. The architecture relies on a probabilistic interpretation of the ANN outputs. Each output unit of the ANN is trained to perform a non-parametric estimate of the posterior probability of a continuous density HMM state given the acoustic observations. After a brief review of HMM and ANN, the paper reports the theoretical aspects and the performance of the proposed hybrid model. Experimental results are listed to demonstrate the potential of this hybrid model.