In this thesis, a two-word, three-word and four-word Mandarin phrases speech recognition system is studied and implemented. This system utilizes hidden Markov model, lexicon search strategy and tone recognition to select the initial phrase candidates and make the final decision. Experimental results indicate that using about one third of the total phrase population, 80%, 92% and 97% correct rates can be achieved for the 70,000 two-word, 24,000 three-word and 22,000 four-word phrases recognition problems respectively. Any spoken phrase can be found within 1 second, using a PC with Intel Celeron 2.4 GHz CPU and Red Hat Linux 9.0 operating system.

This thesis investigates the recognition rate improvement strategies for a Mandarin speech recognition system. Both automatic tone recognition and consonant correction schemes are studied and applied to the Mandarin address inputting system and the Mandarin 2, 3, 4-word phrase recognition systems. For automatic tone recognition scheme, the acoustic properties of the four tones in the Mandarin training database are estimated statistically by 4 sets of parameters within 6 minutes. These automatically generated parameters can greatly increase the tone recognition accuracy, and at the same time reduce the amount of time spent in the manual tone parameter adjustment, that is about 8 hours in general. For consonant correction scheme, the sub-syllable models are developed to enhance the consonant recognition accuracy, and hence further improve the overall correct rate for the whole Mandarin phrases. Experimental results indicate that over 90% correct rate can be achieved for the Mandarin address inputting system with 180 thousand place names by applying the above two schemes. Furthermore, the recognition rates for the Mandarin 2, 3, 4-word phrase recognition systems with 116 thousand phrases in total can be improved from 77%, 94% and 97.5%, to 85%, 96% and 98% respectively.