Use of inductive learning for speech processing

Proposes the use of inductive inference “decision trees” for speech processing applications such as automatic speech recognition, automatic language identification, speech understanding and speaker verification. The aim of this research is to demonstrate that artificial intelligence techniques such as inductive learning can provide an alternative approach to existing speech processing techniques such as dynamic time warping, hidden Markov modelling (HMM) and neural networks. The construction of the decision tree is based on the C4.5 inductive system developed by J.R. Quinlan (1993). The decision tree is generated automatically from the training speech database. The classification is performed 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 attempts 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