Two-stage phone recognition system using articulatory and spectral features

In this paper, we propose a two-stage phone recognition system using articulatory and spectral features. In the first stage, articulatory features are predicted from spectral features using FeedForward Neural Networks (FFNNs). In the second stage, phone recognition is carried out using the predicted articulatory features and spectral features together. FFNNs and Hidden Markov Models are explored for developing the phone recognition models in stage-2. In this work, spectral features are represented by Mel-frequency cepstral coefficients. The performance of the proposed phone recognition systems are analyzed using an Indian language Bengali and TIMIT speech databases. The recognition accuracy of the proposed two-stage models are compared with baseline phone recognition systems developed using spectral features alone. From the results, it is observed that, the proposed two-stage recognition systems outperform their respective baseline systems. Two-stage recognition systems have shown an improvement of 5.87% and 7.55% for Bengali and TIMIT speech databases respectively.