The objective of this thesis is to reduce the training size for the Mandarin address inputting system and the Mandarin phrase recognition system. A set of two-word Mandarin phrases is developed by the balanced sieving and mixture training techniques. This greatly reduces the training data size for the systems. Hidden Markov model using both MFCC and LPCC features is proposed in this thesis. Speech-text alignment, frame overlapping and tone recognition are incorporated to increase the correct recognition rates. For the speaker-dependent case, any phrase in these two speech systems can be recognized within one second.