In this paper, we describe a new algorithm based on the discrete wavelet transform (DWT) which uses a multi-threshold decision model (MTD model) to detect acoustic and phonetic classes (based on 10ms speech signal segments). The best thresholds of the model are found by using experimental pattern classification. Then a unit level interpolation technique is combined with the MTD model to classify phonetic units (based on sequences of 10ms segments). The results of the classifiers are compared and jointly adjusted by an interactive scheme (IS) in order to improve the performance of the algorithm. The algorithm is tested with the TIMIT database and compared with the SUB-CRA-based algorithm and other algorithms to demonstrate its effectiveness.