Japanese text dictation system for official reports

专家们经常出具官方报告，如房地产估价、法医解剖等。做正式报告是一件费时的工作。如果将语音输入自动转换为句子，将减少制作文档的工作量。在目前的语音输入系统中，自动识别连续说出的一般句子仍然很困难。但是，当条件限制到使用者是特殊的时候，1)使用者被限制在指定的说话人之内，2)文件的表达几乎是确定的，句子结构相对简单，3)官方报告中的词汇量从3000个减少到4000个，并且将文件分成各个部分，词汇量减少得更多。这些条件将减轻设备的负载，使设备更容易开发。在这项研究中，我们开发了一个尸检报告的句子识别系统。首先，我们用ECGI方法建立一个自动机来表示句子的结构。然后，我们定义了自动机状态之间的距离，以加强与预期出现的单词的对应关系。基于这一定义，我们发展了一种对自动机进行修正和推广的方法。对于音素识别，我们使用了Niyada开发的模型音方法。我们全体成员将上述方法结合在一起，制作了健全的尸检结果输入系统。系统在没有时间延迟的情况下开始识别声音。由于声音识别的精度还不够高，今后将继续对系统进行改进。

The experts often make official reports such as for estimation of real estiate, for medico-legal autopsy and so on. It is a time-consuming job to make official reports. If speech input is automatically transformed to sentenses, the load of making document will be decreased. In current speech input system, it is still difficult to automatically recognize continuously spoken general sentences. But, when the condition is so limited that a user is special, 1) a user is limited to a specified speaker, 2) the expression of document is almost decided, and the structure of sentence is comparatively simple, 3) the vocabulary number in the official reports is 4000 from 3000, and the vocabulary number decrease more by deviding the document into each of parts. These conditions will lighten load to a device, and the device will be developed with ease.In this research, we have developed a sentense recognition system for autopsy reports. Firstly, we built an automaton by ECGI method to represent the structure of sentence. Then we defined the distance between the states of an automaton to strengthen correspondence to the words, the appearance of which was expected. Based on this definition, we developed a method in which an automaton was revised and generalized. For phoneme recognition, we used the model sound method developed by Niyada. We, all the members, put the above-mentioned methods together and made the sound input system of autopsy findings. The system ran to recognize sounds without time delay. Since the precision of sound recognition is not enough, the improvement of the system will be continued in future.