臺灣博碩士論文加值系統

構音障礙為發音過程中發生錯誤或困難，導致咬字不正確進而造成語句不夠清晰，而構音障礙一直是常見的兒童語言問題，目前在台灣醫療界對於構音障礙的類別並沒有統一說法 ，所以一般醫院的治療方式都必須有一位語言治療師來進行判斷及治療，治療師會針對每種構音障礙類別所缺乏的發音去設計一系列的單字並讓孩童跟著念，經由一連串的單字發音後 治療師會根據該孩童的發音狀況去下判斷，並持續幾個月的回診來改善發音問題，但這樣的治療方式造成構音孩童只能在醫院才能接受治療並得到回饋，造成治療周期拉長，本論文目的是結合最新的AI卷積神經網路（CNN）自動診斷構音障礙。結果顯示，LeNet-5在模型最小的情況下達到94.56 Top-1準確度和0.995平均F1-score使得更適合在移動裝置上執行構音障礙的分類。

Articulation disorder means having difficulties during pronunciations, leading to incorrect articulations and unclear sentences. Articulation disorder has been a common child language issue. Currently, there is no any unified sayings for articulation disorder's classification in the Taiwan's medical field. Thus, a speech therapist is required for analysis and treatment in hospitals. After a series of pronunciations, a speech therapist will make an analysis based on children's pronunciations. Children will return to the hospitals for months continuously to improve their conditions. Nevertheless, the treatment can only benefit children with articulation disorder by receiving treatments in hospitals, slowing down the treatment cycle. The purpose of this work is to automate the diagnosis for articulation disorder by combining the latest AI's convolutional neural network (CNN). Results show that LeNet-5 which achieved 94.56 Top-1 accuracy and 0.995 avg F1-score with the smallest model size is more suitable to apply articulations disorder application on mobile devices.

Table of Contents
Recommendation Form
Committee Form
Chinese Abstract
English Abstract
Acknowledgements
Table of Contents
List of Tables
List of Figures
Introduction
Related Works
Proposed Method
Experimental Results
Conclusions

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