| Summary: | 碩士 === 中原大學 === 資訊工程研究所 === 89 === In this thesis, a speaker-independent Mandarin spoken word recog-nition system for Taiwan railway station is implemented. The system is built with components that include a Pentium PC, Microsoft Windows 98 operation system, Microsoft Visual C++ 6.0 and Intel Recognition Primi-tives Library.
During the acoustic-model training stage, we employ the energy dip, zero crossing rate, and autocorrelation function to segment speech sounds. And use the MFCC (mel scale filter cepstral coefficient) to evaluate fea-ture parameters. Through the process of Binary splitting the vector quan-tization codebooks are found, the DHMM (Discrete Hidden Markov Models) is used to establish all acoustic-models, and the BaumWelch al-gorithm is chosen to adapt the optimal solution. On the recognition part, the Kohonon Network is used to calculate codeword sequence. The Beam search is used to replacement of Viterbi algorithm that gives the best re-sult of recognition in DHMM.
The recognition rates of speaker-independent experiments can reach up to 85.75%. It shows that the system has achieved good performance.
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