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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/69295

    Title: 利用Android系統開發可攜式語音診斷與復健系統;Development of an Android-based speech diagnosis and rehabilitation system
    Authors: 吳柏葦;Wu,Po-Wei
    Contributors: 電機工程學系
    Keywords: Android;構音障礙;智慧型裝置;復健;語音分析;聲譜;頻譜
    Date: 2015-12-30
    Issue Date: 2016-01-05 19:17:50 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 摘要
    本研究利用本實驗室先前與台北榮民總醫院所合作錄製個案的語音資料,透過快速傅立葉轉換 (Fast Fourier Transform, FFT) 及線性預測編碼 (Linear Predictive Coding, LPC) 的聲學量化方法,將語音的時域訊號量化再透過圖形及數據的方式來呈現語音相關的診斷資訊。本研究針對所錄製的中文母音/ㄚ/、/一/、/ㄨ/、/ㄝ/、/ㄛ/以及子音/ㄍ/、/ㄎ/、/ㄏ/、/ㄐ/、/ㄑ/、/ㄒ/、/ㄓ/、/ㄔ/、/ㄕ/來做比較,探討正常語音與構音障礙者之間的差異性,藉由母音共振峰的觀察以及子音在聲譜上的能量分佈,來判別構音障礙者的發音是否正確,從比較結果顯示,使用者可以透過本研究系統能夠觀察出構音障礙者發音的問題所在,除此之外,構音障礙患者也可以運用本系統作自主語音訓練。
    Language is one of the most important communication tool. Recently, patients with articulatory disorders are increasing year by year and causing problems in their communication with other people. Mobile devices, such as smart phone and tablet PC, become more common as the development of technology advances. It would be a great help for patients and speech therapists if we could apply our mobile devices for speech diagnosis and rehabilitation. Therefore, the purpose of this study was to develop an Android-based speech diagnosis and rehabilitation system which could be used to record and compare the speech signals from the normal speaker and the patient with articulation disorder via the user interface. In this user interface, the clinical users could compare these two signals in the forms of speech signal, spectrum, spectrogram, and the fundamental frequency to provide a quantitative analysis for the speech therapist and greater therapeutic effect for patients with articulatory disorders.

    In this study, we analyzed and compared speech recordings, including Chinese vowel and consonants, from the previous cooperative hospital with fast Fourier transform, linear predictive coding and other acoustic quantitative methods to provide useful speech-related diagnostic graphic information. Our results showed that clinical users could observe the difference between normal and disordered speech through our research system with differences in the first three formant frequencies for vowel and energy distribution in the spectrograms for consonants. In addition, patients with articulatory disorders could also use our system for self-training and -learning.

    In order to evaluate the validity, functionality and usefulness of our system, we compared the results of our system with the Praat system and the visible voice diagnosis and rehabilitation system which was previously developed with Matlab in our lab. In summary, the Android-based speech diagnosis and rehabilitation system could show the differences between normal and disordered speech on the mobile devices. The speech therapists could use our system as a diagnostic and assessment tool in the clinical settings, and provide patients with articulatory disorders a better training and rehabilitation tool.
    Appears in Collections:[電機工程研究所] 博碩士論文

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