DISFLUENCY DETECTION ON SPONTANEOUS SPEECH USING LEARNING CELLULAR AUTOMATA
Speech recognition is an efficient tool for interacting between human and machines; but the speech includes some undesirable components (disfluencies), sometimes, and is called spontaneous speech in these cases. This paper focuses on detecting some simple disfluencies in this speech type, using a Learning Cellular Automata. This is a set of cells that each is a learning automata (LA), and selects an action with a determined probability. Each LA is assigned to a word of the speech transcription sentence, and the action set for each LA includes four simple disfluencies types and "Fluent". The environment considered for interacting with cells, evaluates the action selected by each cell in every step, and sends back a response to the LA. That response will be considered for rewarding or penalizing the selected type. Because of parallel operation by the cells, and by selecting appropriate values for rewarding and penalizing coefficients, the proposed model can reach a desirable performance for tagging the disfluencies, regarding the time consumption and precision.
disfluency spontaneous speech learning cellular automata cellular automata learning automata