傳統使用語音資訊之語音辨識系統，在日常生活中的應用已是很常見的，例如：聲控開關；然而，易受雜音干擾則為此類語音辨識系統之最大弊病，即使能夠選用改良之收音器材，如指向性麥克風，以減少雜音干擾之情形。然而，高昂的成本即為設計此系統要面臨之代價。於是，許多學者針對上述之問題，提出了改良方法，包括：以影像資訊為基礎之語音辨識系統，即唇語辨識系統。唇語辨識系統能夠免除於雜音之干擾，甚至可與以語音資訊為基礎之語音辨識系統結合，能夠有效提昇其辨識率。本研究之目的即為設計一唇語辨識系統，結合彩度色彩空間(chromaticity color space)與K-means演算法(K-means algorithm)作為唇形影像切割方式，進而擷取出唇形特徵，並配合隱藏式馬可夫模型的使用，以提昇唇語辨識系統之辨識率。實驗結果將比較不同色彩空間之唇形切割技術，以及不同特徵之辨識率。 Nowadays, the conventional speech recognition system has been used in many applications. However, the conventional speech recognition system would be interfered by the voice noise According to the disturbance, the recognition rate would be decreased in the noise condition. So, researchers proposed the singular visual feature speech recognition system, a lipreading system, to avoid the affection of voice noise. The lipreading system can be the assistance part of the conventional speech recognition system, to raise the speech recognition rate. In our research, we proposed a lipreading system which the lip image segmentation part is chromaticity color space combined with K-means algorithm. And taking the Hidden Markov Model as the recognition part to improve the recognition rate. In the experiment results, our method compared with other color based lip segmentation, and compared the recognition rate of different features.
