基于小波分析的語音端點檢測算法研究

摘 要
語音端點檢測是語音識別中至關(guān)重要的技術(shù)。無論軍用還是民用，語音端點檢測都有著廣泛的應(yīng)用。在低信噪比的環(huán)境中進(jìn)行精確的端點檢測比較困難，尤其是在無聲段或者發(fā)音前后。本文討論了幾種常用的端點檢測方法，并提出兩種基于小波分析的端點檢測，并在此基礎(chǔ)上描述了基于這兩種算法的語音端點檢測綜合算法，從而實現(xiàn)對語音信號精確端點檢測的方法。
文中首先介紹了幾種常見的語音端點檢測方法如短時能量與過零率，隱馬爾可夫等。這些方法在靜音環(huán)境下，當(dāng)噪聲較小或噪聲相對單一時可以取的較好的檢測結(jié)果，但在語音環(huán)境較惡劣、信噪比較低時，檢測的結(jié)果下降較快，難以讓人滿意。為此本文引入了小波變換作為分析工具。接下來論文討論了小波變換的原理及在語音識別系統(tǒng)中的應(yīng)用。
論文分別提出了兩種基于小波系數(shù)的語音端點檢測方法，并對其實驗結(jié)果進(jìn)行了比較。第一種方法是子帶平均能量方差用于語音端點檢測，該方法利用噪聲的分類及特點，以及它與語音信號的差別，在小波分析的基礎(chǔ)上，對每一子帶的平均能量進(jìn)行方差分析，從而區(qū)分出語音段。該方法具有快速、簡單和準(zhǔn)確率高的特點。第二種方法是小波系數(shù)方差用于語音端點檢測，語音信號是統(tǒng)計自相似的隨機過程，它的統(tǒng)計特性在時域內(nèi)不隨波形的擴充或壓縮而變化。根據(jù)這一特性為識別語音與背景噪聲建立一個理想的貝葉斯兩層分類器，以每一子帶內(nèi)的小波系數(shù)作為比較參數(shù)，從而進(jìn)行分類計算。最后根據(jù)概率的大小得到端點檢測的結(jié)果。該方法具有適用范圍廣、準(zhǔn)確率高的特點，而算法相對比前一方法要復(fù)雜。
論文在討論了前兩種方法的優(yōu)缺點、分析實驗結(jié)果后，提出一種揉合兩種方法，以發(fā)揮各自優(yōu)點的新方法。實驗表明該方法發(fā)揮了以上兩種方法的特點具有很好的檢測結(jié)果。

關(guān)鍵詞：端點檢測，小波變換，系數(shù)方差，子帶能量
STUDY OF SPEECH ENDPOINT DETECTION ALGORITHM BASED ON THE WAVELET ANALYSIS
ABSTRACT
Speech endpoint detection is a key technology for speech recognition. It is widely used in not only military usage but also civilian usage. It is difficult to exactly detect endpoint under low SNR, especially in silence segment or before pronouncing or after pronouncing. This paper discussed several kinds of commonly used endpoint detection methods, and proposed two endpoint detection means based on the wavelet transform, and based on this integrated speech end-point detection algorithm, thereby the method of exact speech signal endpoint detection can be obtained.
This paper first introduced some kinds of commonly used speech endpoint detection methods, such as short-time energy and zero-crossing rate, HMM etc. Using these methods, the result is better under silence environment, less noisy or relatively single noisy environment, but under bad, low SNR environment, the decline of the result is fast, and the result is not satisfying. So this article presented using wavelet transform as analysis tool. Next, we discussed the principle of wavelet transform and its application in speech recognition system.
This paper proposed two kinds of speech endpoint detection methods based on wavelet coefficients respectively, and compared the two experimental results. The first method is sub-band average-energy variance used in speech endpoint detection. Based on wavelet analysis, utilizing the classification , character of the noise, and the difference between speech signal and noise, this method made variance analysis on the average energy of each sub-band to distinguish speech endpoint. This method has the feature of fast, simpleness and high exact rate. The second method is using the variance of the wavelet coefficients to detect speech endpoint. Speech signal is a random process with statistical similarity to itself, its statistical feature doesn’t vary with the expansion or compression of the waveform in time field. According to this feature, we establish an ideal Bayes classification models with two levels for recognizing speech and background noise. The classification calculation is based on considering wavelet coefficients variance of each sub-band as comparing parameter. At last, the result of endpoint detection is obtained through comparing their variance. This method has the feature of wide range for applicability and high accuracy, but the algorithm is more complex than the former method.
After discussing the merits and shortcomings of the former two methods and analyzing the experimental results, this paper presented a new method which combined the merits of the former two methods. The experiment indicated that this new method has good detection result for integrating the features of the above two methods.

KEY WORDS：speech endpoint detection，wavelet transform，parameter variance，subband-energy
目 錄
第一章 緒 論 1
1.1概述 1
1.1.1 語音識別簡介 1
1.1.2 端點檢測在語音識別系統(tǒng)中的地位和作用 3
1.1.3 國內(nèi)外研究現(xiàn)狀 5
1.2幾種常用的端點檢測方法 7
1.2.1 短時能量及過零率 8
1.2.2 熵函數(shù) 10
1.2.3 LPC倒譜特征 11
1.2.4 隱馬爾可夫(HMM) 13
1.3 課題研究背景 14
1.4論文內(nèi)容安排 16
第二章 小波分析理論 18
2.1概述及特點 18
2.2小波分析與傅立葉分析的比較 19
2.3小波分析的基本理論 24
2.4 小波分析在語音處理中的應(yīng)用 30
2.5 小結(jié) 33
第三章 子帶平均能量方差用于語..