Articulatory Phonology views speech as an ensemble of constricting events (e.g. narrowing lips, raising tongue tip), gestures, at distinct organs (lips, tongue tip, tongue body, velum, and glottis) along the vocal tract. This study shows that articulatory information in the form of gestures and their output trajectories (tract variable time functions or TVs) can help to improve the performance of automatic speech recognition systems. The lack of any natural speech database containing such articulatory information prompted us to use a synthetic speech dataset (obtained from Haskins Laboratories TAsk Dynamic model of speech production) that contains acoustic waveform for a given utterance and its corresponding gestures and TVs. First, we propose neural network based models to recognize the gestures and estimate the TVs from acoustic information. Second, the "synthetic-data trained" articulatory models were applied to the natural speech utterances in Aurora-2 corpus to estimate their gestures and TVs. Finally, we show that the estimated articulatory information helps to improve the noise robustness of a word recognition system when used along with the cepstral features.