Speech recognition in the real world: artificial neural networks and robustness
Harouna Kabré · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
This paper presents an empirical modeling of the role of environment for Automatic Speech Recognition systems in real world, taken in the framework of an Artificial Life methodology. Environment is modeled as an active system which triggers the shift between the training and testing states of automatic speech recognition systems (ASRSs) which are built from ANNs. First an initial set of ASRSs are created to recognize speech under the constraints of an unpredictable acoustic world. The training of the ASRSs starts and goes on until ASRSs no longer decrease their error classification in the current acoustic environment because of noises. This moment is detected by the reactive environment and the structure of the ASRSs are changed. The simulation performed with mathematical models of real rooms as environment showed that our system could be used as a prediction tool of ASRSs performances for the study of any speech perceiver based on ANNs or on hidden Markov models. Moreover, it is shown that on a task of French digits recognition, the new method performs better than conventional ones.