本論文主要探討半人形機器人在與人對話導覽功能上之設計，發展一套適合於導覽功能的對話策略。為了使機器人能與不同的使用者對話，故本論文以隱藏式馬可夫模型架構，來發展語者獨立的語音辨識系統，其中使用了線性預估倒頻譜參數與梅爾頻率倒頻譜參數，作為語音的特徵參數。接著，提出以兩個模糊邏輯控制器來調整語音端點偵測的兩種參數值。一般端點偵測方法採用固定式參數值，在環境噪音變異量較大的時候，端點偵測的重複搜尋會使得系統執行要花上更多的時間。根據實驗的經驗值可訂出適當的輸入與輸出的歸屬函數並決定模糊推論規則，再根據規則表的推論來計算出適當的參數值。最後，以實驗來證明所設計之語音對話導覽系統的效能與適用性。

The design of human dialogue and guidance capability for the semi-humanoid robot is conferred mainly in this thesis. A dialogue strategy that is adaptable to the guidance capability is developed. In order to make the semi-humanoid able to dialogue with different people, a speaker independent system is developed by using the hidden Markov model (HMM). The linear prediction derived cepstrum coefficient and Mel-frequency cepstrum coefficient are combined as the speech feature parameters. Moreover, two fuzzy logic controllers are presented to adjust a variable, , of speech end-point detection. A fixed is used in general end-point detection. When the variance of environment noise is larger, the time for re-finding the speech end-point may be more. In this situation, the system may take more time to run. The appropriate membership functions of inputs and outputs can be set by utilizing the rule of thumb. The fuzzy inference rule table can then be defined. Next, the appropriate can be calculated according to the inference form rule table. Finally, the efficiency and feasibility of the proposed system is demonstrated by practical experiments.

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