計(jì)算機(jī)理論論文漢語孤立字語音識(shí)別技術(shù)的研究

1、漢語孤立字語音識(shí)別技術(shù)的研究 漢語孤立字語音識(shí)別技術(shù)的研究 study on isolated mandarin speech recognition technology 【摘要】 人類有個(gè)理想,讓機(jī)器具有“聽”、“說”人類語言的能力。這個(gè)理想,在信息時(shí)代正逐步變成現(xiàn)實(shí)。語音識(shí)別正是解決機(jī)器“聽”懂人類語言的一項(xiàng)研究。孤立詞語音識(shí)別實(shí)現(xiàn)簡單、技術(shù)成熟,有著廣泛的應(yīng)用前景,是深入研究語音識(shí)別的基礎(chǔ)。本文對(duì)小詞匯量、非特定人的漢語孤立詞的語音識(shí)別技術(shù)進(jìn)行了分析和研究。首先介紹了語音識(shí)別系統(tǒng)的組成和識(shí)別原理,并對(duì)語音信號(hào)的預(yù)處理過程、端點(diǎn)檢測(cè)常用的特征參數(shù)以及語音識(shí)別的方法作了分析,重點(diǎn)討論了mf

2、cc特征參數(shù)的提取。繼而重點(diǎn)研究了孤立詞的端點(diǎn)檢測(cè)算法,并在基于信息熵、子帶譜熵和頻帶方差的端點(diǎn)檢測(cè)算法的基礎(chǔ)上,對(duì)原有算法做了修正和改進(jìn),仿真結(jié)果表明,在不同噪聲下,基于改進(jìn)的端點(diǎn)檢測(cè)算法在低信噪比條件下的檢測(cè)準(zhǔn)確率明顯高于傳統(tǒng)的基于能量和過零率的雙門限檢測(cè)算法,其中基于改進(jìn)的頻帶方差的檢測(cè)效果最好。最后深入研究了基于dtw和hmm的語音識(shí)別方法。其中基于dtw的高效算法具有運(yùn)算復(fù)雜度低的特點(diǎn),仿真結(jié)果表明,它非常適合于小詞匯量、特定人的語音識(shí)別,識(shí)別率可以達(dá)到100%。但是對(duì)于非特定人識(shí)別,本文選用主流的基于hmm的識(shí)別方法,并仔細(xì)探討了chmm應(yīng)用到語音識(shí)別中的具體問題。結(jié)合改進(jìn)的端點(diǎn)檢

3、測(cè)算法并采用基于chmm的語音識(shí)別方法,最終實(shí)現(xiàn)了對(duì)自建的漢語孤立數(shù)字語音庫92%的平均識(shí)別率?！綼bstract】 man has long dreamed of having a machine that can “l(fā)isten to” and “speak” human languages. this ideal of man, in the information era, is gradually becoming a reality with the state-of-the-art technology in speech recognition, the task of whi

4、ch is to solve the problem of machine understanding the human speech.isolated-word speech recognition is the foundation of further deep research on speech recognition, which is easy to implement, with its technique mature and its application prospect broad. in this paper, the technique of small-voca

5、bulary speaker-independent isolated-word speech recognition is analyzed and researched.firstly, this paper focuses on the introduction of the fundamentals of speech recognition. the components and principles of a typical speech recognition system is presented in simple, then the speech signal prepro

6、cess, the endpoint detection feature parameters and the speech recognition methods are analyzed, further the extraction of mel frequency cepstrum coefficients (mfcc) feature is discussed in detail.secondly, the isolated-word endpoint detection algorithms are mainly researched. based on the endpoint

7、detection algorithms of information entropy, band-partitioning spectral entropy and variance of frequency, revisions and ameliorations are made on the original algorithms and corresponding improved endpoint detection algorithms are proposed, the simulation results under the same snr conditions show

8、that the detection accuracy rate of the improved endpoint detection algorithms is significantly higher than that of the traditional threshold detection algorithm based on energy and zero-crossing, wherein the detection performance of the improved variance of frequency based algorithm is the best.fin

9、ally, speech recognition methods based on dynamic time warping (dtw) and hidden markov model (hmm) are deeply studied. the fast dtw algorithm has low complexity and is very suitable for small-vocabulary speaker-dependent speech recognition. the experimental data shows that its correct identification

10、 rate is almost up to 100%. for speaker-independent speech recognition, hmm-based mainstream identification methods is used in this paper, the specific issues of continuous hmm applied to speech recognition are also discussed. ultimately, combining the improved endpoint detection algorithms with con

11、tinuous hmm recognition method, an average recognition rate of up to 92% is achieved in the recognition of self-built chinese figures voice database. 【關(guān)鍵詞】 孤立詞識(shí)別; 非特定人; 端點(diǎn)檢測(cè); 美爾頻率倒譜參數(shù); 動(dòng)態(tài)時(shí)間規(guī)整; 隱馬爾可夫模型 【key words】 isolated-word recognition; speaker-independent; endpoint detection; mel frequency cepst

12、rum coefficients; dynamic time warping; hidden markov model 漢語孤立字語音識(shí)別技術(shù)的研究摘要 6-7 abstract 7-8 第1章 緒論 11-16 1.1 語音識(shí)別的基本概念 11-12 1.2 語音識(shí)別技術(shù)的發(fā)展歷程 12-13 1.3 語音識(shí)別研究現(xiàn)狀和面臨的挑戰(zhàn) 13-15 1.4 論文的研究內(nèi)容和結(jié)構(gòu)安排 15-16 第2章 語音識(shí)別的基本原理 16-33 2.1 語音信號(hào)的產(chǎn)生及數(shù)學(xué)模型 16-18 2.2 語音識(shí)別系統(tǒng)的組成及其識(shí)別原理 18-19 2.3 語音信號(hào)的預(yù)處理 19-22 2.3.1 語音信號(hào)數(shù)字化 1

13、9 2.3.2 預(yù)加重處理 19-20 2.3.3 加窗和分幀處理 20-22 2.4 端點(diǎn)檢測(cè)常用的特征參數(shù) 22-26 2.4.1 時(shí)域特征參數(shù) 23-24 2.4.2 頻域特征參數(shù) 24-26 2.5 特征提取 26-32 2.5.1 線性預(yù)測(cè)倒譜系數(shù)(lpcc) 27 2.5.2 美爾頻率倒譜參數(shù)(mfcc) 27-32 2.6 語音識(shí)別方法簡介 32-33 第3章 基于孤立詞的端點(diǎn)檢測(cè)算法研究 33-48 3.1 噪聲源和信噪比 33-35 3.2 語音端點(diǎn)檢測(cè)算法及其改進(jìn) 35-48 3.2.1 基于短時(shí)能量和短時(shí)過零率的語音端點(diǎn)檢測(cè)方法 35-36 3.2.2 基于信息熵的語音端點(diǎn)檢測(cè)方法及其改進(jìn)算法 36-41 3.2.3 基于改進(jìn)的子帶譜熵的端點(diǎn)檢測(cè)算法 41-44 3.2.4 基于能量加權(quán)的頻帶方差的端點(diǎn)檢測(cè)算法 44-48 第4章 語音識(shí)別算法研究 48-69 4.1 動(dòng)態(tài)時(shí)間規(guī)整(dtw)算法 48-56 4.1.1 dtw算法的匹配原理 48-52 4.1.2 一種改進(jìn)的高效dtw算法 52-55 4.1.3 dtw模板訓(xùn)練和識(shí)別 55-56 4.2 隱馬爾可夫模型(hmm)在語音識(shí)別中的運(yùn)用 56-69 4.2.1 隱馬爾可夫模型 57-58 4.2.2 hmm中的三個(gè)基本問題及其解決方案 58-63 4.2.3 hmm在語音識(shí)別應(yīng)用中的具體問題 63