There are a lot of difficulties that have to be overcome in the speaker-independent (S.I.) phrase recognition system . And the feasibility of accurate ,real-time and robust system pose of the greatest challenges in the system. In this thesis ,the speaker-independent phase recognition system is based on Hidden Markov Model (HMM). HMM has been proved to be of great value in many applications, notably in speech recognition. HMM is a stochastic approach which characterizes many of the variability in speech signal. It applys the state-of-the-art approach to Automatic Speech Recognition .

A design of speech recognition system for Chinese names has been established in this thesis. By identifying surname first, that is an unique feature of the Chinese names, the classification accuracy and computational time of the system can be greatly improved. This research is primarily based on hidden Markov model (HMM), a technique that is widely used in speech recognition. HMM is a doubly stochastic process describing the ways of pronumciation by recording the state transitions according to the time-varing properties of the speech signal. The results of the HMM are compared with those of the segmental probability model (SPM) to figure out better option in recognizing base-syllables. Under the conditions of equal segments, SPM not only suits Mandarin base-syllable structure, but also achieves the goal of simplifying system since it does not need to find the best transformation of the utterance. A speaker-independent 3000 Chinese names recognition system has been implemented based on the Mandarin microphone database recorded in the laboratory environment.

A speech recognition system for Chinese names based on Karhunen Loeve transform (KLT), MFCC, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. KLT is the optimal transform in minimum mean square error and maximal energy packing sense to reduce data. HMM is a stochastic approach which characterizes many of the variability in speech signal by recording the state transitions. For the speaker-dependent case, the correct identification rate can be achieved 93.97% within 3 seconds in the laboratory environment.

A Mandarin speech recognition system for addresses based on MFCC, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. HMM is a doubly stochastic process describing the ways of pronunciation by recording the state transitions according to the time-varing properties of the speech signal. In order to simplify the system design and reduce the computational cost, the mono-syllable structure information in Mandarin is used by incorporating both mono-syllable recognizor and HMM for our system. For the speaker-dependent case, Mandarin address inputting can be accomplished within 60 seconds and 98% correct identification rate can be achieved in the laboratory environment.

A novel phrase recognition method is proposed. It eliminates the speech difference between intraspeaker or interspeaker by transform phrases to difference subspace. A new endpoint detection method is also proposed, it can detection the human speech signal more effectively. All methods are test and verify at Microsoft Windows environment.

A Mandarin keyword spotting system based on MFCC, discrete-time HMM and Viterbi algorithm with DTW is proposed in this thesis. Joining with a dialogue system, this keyword spotting platform is further refined to a prototype of natural speech patient registration system of Kaohsiung Veterans General Hospital. After the ID number is asked by the computer-dialogue attendant in the registration process, the user can finish all relevant works in one sentence. Functions of searching clinical doctors, making and canceling registration are all built in this system. In a laboratory environment, the correct rate of this speaker-independent patient registration system can reach 97% and all registration process can be completed within 75 seconds.

The objective of this thesis is to build a phrase recognition system under noisy environment that can be used in real-life. In this system, the noisy speech is first filtered by the enhanced spectral subtraction method to reduce the noise level. Then the MFCC with cepstral mean subtraction is applied to extract the speech features. Finally, hidden Markov model (HMM) is used in the last stage to build the probabilistic model for each phrase. A Mandarin microphone database of 514 company names that are in Taiwan¡¦s stock market is collected. A speaker independent noisy phrase recognition system is then implemented. This system has been tested under various noise environments and different noise strengths.

A Mandarin phrase recognition system based on MFCC, LPC scaled excitation, vowel model, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. HMM, which is broadly used in speech recognition at present, is adopted in the main structure of recognition. In order to speed up the recognition time, we take advantage of stability of vowels in Mandarin and incorporate with vowel class recognition in our system. For the speaker-dependent case, a single Mandarin phrase recognition can be accomplished within 1 seconds on average in the laboratory environment.