A Study on Ambiguous Utterance Understanding for Speech Input

提出了一种基于序列级联训练的无监督说话人自适应方法，该方法利用连续参数HMM的最大后验概率估计(MAPE)理论，只需为发音指定音节标记序列。标签序列由预先使用说话人无关模型的识别器自动提供。在连续语音识别上的实验结果表明，该模型具有与监督自适应相当的性能。其次，提出了一种处理感叹词和未登录词的方法，使语音识别系统能够处理对话中的自发语音。我们使用包含感叹词和未登录词的测试句集对我们的语音识别系统进行了性能评估，证实了该方法的有效性。再次，我们研究了一个能够理解所有用户输入的菜单导引的口语自然语言理解系统。这项工作的动机是以下事实，即用户无法理解用自然语言对计算机说什么或如何说。系统显示由可接受的内容词组成的菜单，并且USUR从菜单中选择一个词并说出包括该词的短语。实验表明，我们的系统对于新手用户来说表现良好。

We proposed an unsupervised speaker adaptation method on sequencial concatenation training that used the theory of MAPE(Maximum A Posteriori probabitity Estimation) for continuous parameter HMM.In this method, we should only specify the syllable label sequence for the utterrance. The label sequences were provided automatically by the recognizer which used a speaker-independent model in advance. The experimental results on continuous speech recognition showed that the better model gave a performance comparable to that of supervised adaptation.Secondly, we proposed a method to process interjection and unknown words so that a speech recognition system could deal with spontaneous speech in dialog. We have evaluated the peerformance of our speech recognition system using test sentence sets including interjection or unknown words, and confirmed that the proposed method worked well.Thirdly we investigated the menu-guided spoken natural language understanding system that could understand all user's inputs. This work was motivated by the following fact that a user could not understand what to say or how to say to a computer in natural language. The system displays a menu that consists of acceptable content words and the usur chooses one word from the menu and speaks out phrase that includes the word. The experimental showed that our system performed well for the novice users.