Pulasinghe Koliya, Watanabe Keigo, Izumi Kiyotaka, Kiguchi Kazuo
Department of Advanced Systems Control Engineering, Saga University, Saga 840-8502, Japan.
IEEE Trans Syst Man Cybern B Cybern. 2004 Feb;34(1):293-302. doi: 10.1109/tsmcb.2003.811511.
We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands. The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands. Significance of the words, i.e., the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire. Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system. The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands.
我们提出了一种使用语音命令控制机器的方法。研究了与机器语音接口相关的两个主要问题，即具有模糊含义的词的解释以及自然对话中的词汇外（OOV）词。本文提出的系统旨在克服使用语音命令控制机器时的上述两个问题。当前系统由基于隐马尔可夫模型（HMM）的自动语音识别器（ASR）、用于从正在运行的话语中捕获机器敏感词的关键词识别系统以及基于模糊神经网络（FNN）的控制器组成，该控制器用于表示语音命令中具有模糊含义的词。将词的重要性，即根据机器当前状态的词的上下文含义引入系统，以获得更符合用户期望的实际输出。还考虑了系统的模块化，以便为具有异构功能的系统提供该方法的通用性，而不会降低系统的性能。通过使用语音命令实时导航移动机器人对所提出的系统进行了实验测试。
