Isolated voiced digit recognition using inductive inference

This paper proposes the use of inductive inference “decision trees” for isolated digit recognition. The aim of this research is to demonstrate that inductive learning can provide an alternative approach to existing automatic speech recognition techniques such as dynamic time warping (DTW), hidden Markov modelling (HMM) and neural networks (NN). The construction of the decision tree is based on C4.5 inductive system developed by Quinlan (1986, 1993), The decision tree is generated automatically from the training speech database. The database contains labelled examples in the form of a feature vector and its corresponding label, for each frame. The feature vector may consist of any number of different feature sets and the label may be at the word, phonetic class or phoneme level. The recognition is performed at the frame level, using an inference engine to execute the decision tree and classify the firing of the rules. The proposed system has two main advantages. Firstly, it uses the data-driven approach to isolated word classification, thus attempting to solve the problem of inter and intra speaker speech variability, by the use of a large speech database. Secondly, it has the ability to generate decision trees using any combination of features (parametric or acoustic-phonetic). This allows the integration of features from existing signal processing techniques, that are currently used in HMM stochastic modelling, and acoustic-phonetic features, which have been the cornerstone of traditional knowledge based techniques. Isolated digit recognition results for Texas Instruments (TI) digit database, for speaker dependent and independent recognition, are presented