手语识别是通过计算机提供一种有效而准确的机制将手语翻译成文本或语音.目前最新发展水平的手语识别系统在实际应用中应解决非特定人连续手语问题.提出一种将连续手语识别分解成各孤立词识别的分治方法,用于非特定人连续手语识别.把精简循环网(simple recurrent network,简称SRN)作为连续手语的段边界检测器,把SRN分段结果作为隐马可夫模型(hidden Markov models,简称HMM)框架中的状态输入,在HMM框架里使用网格Viterbi算法搜索出一条最佳手语词路径.实验结果表明,该方法的识别效果比单纯使用HMM要好.

Sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech. State-of-the-Art sign language recognition should be able to solve the signer-independent continuous problem for practical applications. In this paper, a divide-and-conquer approach, which takes the problem of continuous CSL (Chinese sign language) recognition as subproblems of isolated CSL recognition, is presented for signer-independent continuous CSL recognition. In the proposed approach, the SRN (simple recurrent network) is used to segment the continuous CSL. The outputs of SRN are regarded as the states of HMM (hidden Markov models) in which the lattice Viterbi algorithm is employed for searching the best word sequence. Experimental results show that SRN/HMM approach has better performance than the standard HMM.

国家自然科学基金资助项目(69789301);国家863高科技发展计划资助项目(863-306-ZD03-01-2);中国科学院百人计划资助项目