Connected Digit Recognition Using Inductive Inference

This paper proposes a novel approach to connected digit recognition by the use of inductive inference "decision trees". To develop the production rules, the expert is bypassed and instead the classification models are generated inductively by examining a large speech database and then generalising the pattern from the specific examples. This approach has already been successfully used for isolated digit recognition [Samouelian, 1996]. The aim of this research is to demonstrate that inductive learning can provide a viable alternative approach to existing automatic speech recognition (ASR) techniques. The proposed system uses Mel frequency cepstral coefficients (MFCC) front-end signal processing techniques . The C4.5 inductive system [Quinlan, 1993] generates the decision tree automatically from labelled examples in the training database. The recognition is performed at the frame level, using an inference engine to execute the decision tree and classify the firing of the rules. A sorting routine is then used to identify the digit string. Connected digit recognition results for Texas Instruements (TI) digit database, for speaker dependent and independent recognition, for known and unknown digit string lengths are presented.