基於麥克風陣列的語者辨識系統設計與實作

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姓名

游仁男(Jen-Nan Yu) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

基於麥克風陣列的語者辨識系統設計與實作
(Design and Implementation of a Microphone Array Based Speaker Recognition System)

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摘要(中)

為了提升單一麥克風語者辨識系統的效能。本研究因此設計一個基於麥克風陣列的嵌入式語者辨識系統，系統分成四個模組：麥克風陣列聲音訊號擷取、波束成形、語者特徵擷取和語者辨識模組。聲音訊號模組經由微機電(MEMS)麥克風組成的環形麥克風陣列收集語者聲音資訊；波束成形模組藉由多通道聲音處理來增強語音訊號與除去周圍的雜訊；在語者特徵擷取模組，我們使用線性預測編碼倒頻譜(LPCC)來表示語者的聲音特徵模型；最後藉由機率神經網路(PNN)分類器來進行語者辨識。我們建置一個實驗的語者聲音資料庫，錄製十二人共120個相同語句的聲音資料，來驗證此一語者辨識系統，實驗過程藉由機率神經網路平滑參數與波束成形參數的訓練來最佳化辨識率。實驗結果顯示，基於麥克風陣列的語者辨識系統，相較於單一麥克風的語者辨識系統，可降低約百分之十的錯誤相等率。

摘要(英)

The study is to design an embedded speaker identification system based on microphone array in order to improve the efficiency of single microphone identification systems. The system is composed of four modules including sound signal extraction from microphone array, beam forming, speaker features extraction and speaker identification module. Sound signal module is to collect speaker sound information by using loop microphone array composed of Micro Electro Mechanical System (MEMS) microphone; Beam forming is to enhance sound signal and remove background noise via multi-channel sound processing; Linear Predictive Cepstrum Coefficient (LPCC) is applied to represent a speaker sound characteristics module; The classifier of Probabilistic Neural Network (PNN) is applied to identify speaker. Besides, we built a database of experimental speaker sounds with one hundred and twenty same statements recorded by twelve people. This is to validate the speaker identification system. The recognition rate was optimized by PNN smoothing parameters and beam forming parameters during the training. The test results showed that our speaker identification system based on microphone array could reduce about 10% error rate compared to the single one.

關鍵字(中)

★ 語者辨識
★ 麥克風陣列
★ 機率神經網路

關鍵字(英)

★ Speaker Recognition
★ Microphone Array
★ Probabilistic Neural Network

論文目次

第一章緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 論文架構 5
第二章 MEMS麥克風陣列波束成形 6
2.1 MEMS 麥克風 6
2.1.1 MEMS 麥克風的原理 7
2.1.2 MEMS麥克風的種類 7
2.1.3 麥克風的指向性 8
2.2 麥克風陣列 10
2.2.1 線狀麥克風陣列 10
2.2.2 環形麥克風陣列 11
2.3 波束成形演算法 12
2.3.1 延遲求和波束成形(Delay and Sum Beamformer) 12
2.3.2 利用GCC-PHAT 估算TDOA（Time Difference of Arrival） 14
2.4 聲源方位估測演算法 15
2.4.1 到達時間差(TDOA)聲源方位估測法 15
2.5 特徵擷取 16
2.5.1 前處理 16
2.5.2 線性預測倒頻譜係數(LPCC) 19
2.6 機率神經網路(PNN)分類器 20
2.6.1 機率神經網路架構 20
第三章麥克風陣列語者辨識系統 22
3.1 系統架構 23
3.1.1 聲音訊號擷取 24
3.1.2 波束成形 25
3.1.3 語音特徵擷取(feature extraction) 26
3.1.4 語者辨識 27
3.2 散事件系統建模 28
3.2.1 麥克風陣列語者辨識系統建模 28
3.2.2 聲音訊號擷取建模 29
3.2.3 波束成形建模 30
3.2.4 語音特徵擷取建模 31
3.2.5 語者辨識建模 32
3.2.6 主要的狀態(state)與動作(action) 33
3.3 軟體合成 35
3.3.1麥克風陣列語者識系統模型軟體合成 36
3.3.2聲音訊號擷取模型軟體合成 37
3.3.3波束成形模型軟體合成 37
3.3.4語音特徵擷取模型軟體合成 38
3.3.5語者辨識模型軟體合成 39
3.3.6軟體的模擬 40
第四章系統整合實驗與驗證 45
4.1實驗環境 45
4.1.1 STM32F429 Discovery 開發板規格簡介 45
4.1.2 MEMS麥克風規格簡介 48
4.2實驗 48
4.2.1 受測人員資料採集 49
4.2.2 麥克風陣列語者辨識系統樣本與參數的訓練 51
4.3語者辨識性能評估 54
4.3.1 單一麥克風的語者辨識效能 55
4.3.2 使用麥克風陣列的語者辨識效能 55
4.4 實驗結果與討論 56
第五章結論 57
參考文獻 59

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2017-7-24

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