臺灣博碩士論文加值系統

在日常生活上，聽障人士僅能運用手語、唇語，或是利用紙筆作為溝通的工具。至今，針對聽障人士仍無一個有效的輔具來改善溝通上的不便。因此，建構一可攜式的手部動作轉語音系統，消除聽障人士溝通上的困難，為本研究主要的目的。

本研究在語音模組中先利用Labview圖控操作軟體中的錄音程式，以8kHz的採樣率、16bit的形式將語音訊號擷取出，再以多脈衝激勵線性預測編碼(MPLPC)壓縮編碼，再將編碼訊息與語音程式結合存放於語音合成晶片(SPDS105A)中；控制模組中利用七通道肌電訊號擷取系統由MSP430F148內建之A/D，擷取手前臂肌電訊號，並透過三種基本特徵值處理，正規化後經前向式類神經網路辨識，同時將結果輸出至語音模組。系統測試實驗中，定義了7種手部動作，對象為四男二女，平均年齡23±2歲，測試時使測試者坐著，以右手配戴主動電極腕帶，每種手部動作重複作十次與二十次。本研究分別Matlab及 MSP430F148進行辨識，當一半為訓練樣本，一半當為測試樣本時，Matlab方式的辨識結果為77.13﹪及87.85﹪，而MSP430F148的辨識結果為64.75﹪及70.28﹪，兩種方式結果顯示，訓練樣本愈多，辨識率愈高；另一方面，Matlab離線分析的辨識率都比MSP430F148辨識率高，顯示定點數的運算使類神經網路產生較大的誤差，造成辨識率降低；另一方面系統手部動作即時辨識，辨識率只有32.86﹪，歸納原因有前向式類神經網路無學習的能力、電極產生位移、人為因素、MSP430F148運算精確度。

整體而言，本系統已有初步的系統架構，但本系統手部動作辨識率仍不足，這其中有許多問題尚待解決，包含電極重新設計，在單晶片上實現倒傳遞神經網路，相信對於系統的辨識率能大大的提升。

The hearing impaired person can make use of sign language, lip reading or paper and pen as tools to communicate with other. Until now, there is still no effective tool to improve their communication ability. Thus, to develop a portable system to translate hand motions into speech is the goal of this study.

In the voice module, the speech is digitized using Labview voice recording program with a sampling rate of 8000Hz and 16-bits format. The digitized speech is then coded using multi-pulse linear predictive coding (MPLPC) and programmed into speech synthesis chip (SPDS105A). In control module, using seven-channel EMG acquisition system and the build-in analog-to-digital converter in the MSP430F148, the forearm EMG signals are acquired. EMG characteristics are obtained using three feature extraction methods and normalized before inputted into the forward neuron network for identification. In the same time, the result is outputted to the voice module. To evaluate the proposed system, seven hand motions are defined. Six subjects are recruited including four male and two female. The average age is 23±2. During the experiment, subject sits in a chair and wearing the active electrodes for EMG measurement in their right arm. Every designated hand motions are repeated for 10 or 20 times. Both Matlab method and MSP430F148 are used for classification. When half of the EMG signals are used for network training and the other half are used for testing, the results of classification using Matlab are 77.13﹪and 87.85﹪. On the other hand, the results of classification using MSP430F148 are 64.75﹪and 70.28﹪. The results indicates that the more the training samples are the better the classification. On the other hand, the results using Matlab methods are better than MSP430F148. This indicates that the fix-point computation in neural network cause error and reduce the rate of classification. Additionally, in the test of real-time classification, the classification rate is 32.86﹪. The reasons for this low percentage may include the lack of learning ability in the forward neuron network that is used in this study, the displacement of electrodes, the error caused by fix-point computation and other human factors.

Although the proposed system has the basic structure, the classification rate for hand motion is still too low. There are still a lot of questions to be resolved including redesigning the active electrode, realizing back-propagation neuron network in the micro-controller. It is believed that with all these improvements the identification rate can be increased.

目 錄

摘要…………………………………………………………………Ⅰ
英文摘要……………………………………………………………Ⅱ
謝誌…………………………………………………………………Ⅳ
目錄…………………………………………………………………Ⅴ
圖索引………………………………………………………………Ⅶ
表索引………………………………………………………………Ⅸ
第一章 緒論……………………………………………………….1
1-1 前言…………………………………………………………..1
1-2 研究目的……………………………………………………..2
1-3 論文架構……………………………………………………..3
第二章 研究背景與原理………………………………………….4
2-1 文獻回顧……………………………………………………..4
2-2 肌電訊號的特性……………………………………………..6
2-2-1 肌電圖………………………………………………......6
2-2-2 肌電訊號特徵值……………………………………......7
2-3 類神經網路…………………………………………………..8
2-3-1 類神經網路簡介……………………………………......8
2-3-2 倒傳遞神經網路…………………………………….....10
2-4 語音壓縮編碼……………………………………………….12
2-5 發展環境與工具程式…..………………………………….14
第三章 研究方法…………………………………………………16
3-1 肌電訊號擷取……………………………………………….17
3-2 硬體架構…………………………………………………….17
3-2-1 語音模組…………………………………………….....17
3-2-2 控制模組…………………………………………….....20
3-3 軟體架構…………………………………………………….25
3-3-1 語音模組…………………………………………….....25
3-3-2 控制模組…………………………………………….....27
3-4 手部動作設計……………………………………………….31
3-5 系統驗證…………………………………………………….32
3-6 實驗設計…………………………………………………….33
第四章 結果與討論………………………………………………35
4-1 硬體測試結果……………………………………………….35
4-1-1 語音模組…………………………………………….....35
4-1-2 控制模組…………………………………………….....36
4-1-3 系統硬體驗證……………………………………….....37
4-2 軟體測試部分……………………………………………….37
4-2-1 語音模組…………………………………………….....37
4-2-2 控制模組…………………………………………….....39
4-3 辨識測試…………………………………………………….41
第五章 結論與未來展望…………………………………………55
參考文獻………………………………………………………….57
附錄……………………………………………………………….59

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