Solutions for robust recognition over the GSM cellular network

This paper deals with automatic speech recognition robustness for noisy wireless communications. We propose several solutions to improve speech recognition over the cellular network. Two architectures are derived for the recognizer. They are based on Hidden Markov Models (HMMs) adapted to adverse noise conditions. Then two more specific solutions aiming to alleviate GSM cellular network defects (holes and impulsive noise) are developed. Holes are detected and rejected. Impulsive noises are modeled using mixture density HMMs and a maximum likelihood criterion. These solutions allow a noticeable recognition error reduction. The last one seems to be promising.