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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/119801

    Title: 基於i-Vector 特徵之聲音風格分析
    Analysis of Voice Styles Using i-Vector Features
    Authors: 高文聰
    Kao, Wen-Tsung
    Contributors: 廖文宏
    Liao, Wen-Hung
    Kao, Wen-Tsung
    Keywords: 聲音風格
    Sound style
    Machine learning
    Pattern recognition
    Date: 2018
    Issue Date: 2018-08-29 16:04:21 (UTC+8)
    Abstract: 聲音的風格有若干常見的形容詞,但難以被精確定義。本論文試圖從語者辨識(Speaker Recognition)的觀點出發,針對不同的聲音風格進行分析,使用的方法為目前在語音辨識中常用的特徵值向量i-Vector,並搭配支援向量機(SVM)做分類。為了測試i-Vector對於聲音風格描述的可用性,在過程中我們事先做了許多的驗證,包含基本語者辨識、最短輸入聲音長度測試、白噪音對於語者驗證的影響、說話內容關聯性測試、聲音取樣率測試與配音員使用不同聲調對於風格的測試。確認特徵之相關性後,我們挑選日常生活中常見的八種聲音風格類型進行分類,分析結果是否具一致性,證實利用語者辨識系統也可以有效的辨識聲音的風格類型。
    Many adjectives have been used to describe voice characteristics, yet it is challenging to define sound styles precisely using quantitative measure. In this thesis, we attempt to tackle the sound style classification problem based on techniques designed for speaker recognition. Specifically, we employ i-Vector, a widely adopted feature in speaker identification together with support vector machine (SVM) for style classification. In order to verify the reliability of i-vector, we conducted a series of experiments, including basic speaker recognition function, minimum voice duration¸ noise sensitivity, context dependency, sensitivity to different sampling rates and style classification of samples from voice actors. The results indicate that i-Vector can indeed be utlilized to classify sound styles that are commonly perceived in daily life.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0103971014
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.EMCS.007.2018.B02
    Appears in Collections:[資訊科學系碩士在職專班] 學位論文

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