本文選題：語(yǔ)音端點(diǎn)檢測(cè) + 能量聚類　； 參考：《上海師范大學(xué)》2017年碩士論文

【摘要】：語(yǔ)音端點(diǎn)檢測(cè)的目的是檢測(cè)出語(yǔ)音信號(hào)中的語(yǔ)音與非語(yǔ)音片段。在很多先進(jìn)的語(yǔ)音處理應(yīng)用的前端處理部分,比如語(yǔ)音識(shí)別,聲紋識(shí)別和語(yǔ)音傳輸,語(yǔ)音端點(diǎn)檢測(cè)都是重要的步驟。在所有語(yǔ)音端點(diǎn)檢測(cè)系統(tǒng)中,基于能量的語(yǔ)音端點(diǎn)檢測(cè)最常被使用。基于能量的語(yǔ)音端點(diǎn)檢測(cè)在無(wú)噪聲環(huán)境下性能較好,但是在噪聲環(huán)境下性能下降較多。自適應(yīng)語(yǔ)音端點(diǎn)檢測(cè)與傳統(tǒng)的基于能量的語(yǔ)音端點(diǎn)檢測(cè)相比,具有很多方面的優(yōu)勢(shì)。然而,自適應(yīng)語(yǔ)音端點(diǎn)檢測(cè)中,唯一的最低能量門限不能適應(yīng)不同的噪聲背景。本文的第一個(gè)研究?jī)?nèi)容,提出了一種方法改進(jìn)這個(gè)問(wèn)題,一種基于k-means的平均能量聚類方法,可以為每個(gè)語(yǔ)音找到更適合的最低能量門限。此外,實(shí)驗(yàn)中還使用了中值濾波,以平滑短時(shí)噪音產(chǎn)生的干擾。在NIST SRE2006說(shuō)話人測(cè)評(píng)(SRE)數(shù)據(jù)上的實(shí)驗(yàn)表明,我們提出的方法比傳統(tǒng)基于能量的VAD和自適應(yīng)VAD均能獲得更好的性能�；谏疃壬窠�(jīng)網(wǎng)絡(luò)的語(yǔ)音端點(diǎn)檢測(cè)方法由于性能顯著優(yōu)于其他方法,成為近期的研究焦點(diǎn)。本文的第二個(gè)研究?jī)?nèi)容,以一種基于深度神經(jīng)網(wǎng)絡(luò)的語(yǔ)音端點(diǎn)檢測(cè)方法為基礎(chǔ),針對(duì)其在低信噪比環(huán)境中表現(xiàn)不佳的問(wèn)題和易受短時(shí)噪音干擾的問(wèn)題,分別使用了譜減法語(yǔ)音增強(qiáng)和自適應(yīng)中值濾波的方法做了改進(jìn)。另外,本實(shí)驗(yàn)提出一種監(jiān)督學(xué)習(xí)規(guī)則,類比于人類學(xué)習(xí)先易后難的原則對(duì)神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練,顯著加快了神經(jīng)網(wǎng)絡(luò)的收斂速度。在AURORA2數(shù)據(jù)庫(kù)上的實(shí)驗(yàn)結(jié)果表明,相比于基線系統(tǒng),改進(jìn)后的方法不僅加速了訓(xùn)練速度,而且還取得了31.12%的相對(duì)性能提升。
[Abstract]:Speech endpoint detection is to detect the speech signal in speech and non speech segments. In many advanced voice processing application front-end processing, such as speech recognition, voice recognition and voice transmission, speech endpoint detection is an important step in all speech endpoint detection system, speech endpoint detection is often the most energy by using the energy based endpoint. Better detection performance in noise environment based on performance, but in the noise environment decreased more. The traditional speech endpoint detection and adaptive speech endpoint detection based on energy ratio, has many advantages. However, adaptive speech endpoint detection, can not only meet the minimum energy threshold noise is different. The first research content, proposes a method to improve this problem, an average energy clustering method based on k-means, You can find the lowest energy threshold is more suitable for each speech. In addition, the median filter is used to smooth short-term interference noise. In NIST SRE2006 (SRE) speaker evaluation indicates that the data on the experiment, we propose a method based on energy performance than the traditional VAD and adaptive VAD can get better. Speech endpoint detection method based on neural network depth due to performance significantly better than other methods, has become the focus of research in recent years. Second the research content of this paper, in a speech endpoint detection method based on deep neural network based on the low SNR environment of poor performance and vulnerable to short-term noise interference the problem, using method of spectral subtraction speech enhancement and adaptive median filtering is improved. In addition, this study proposes a supervised learning rules, analogous to the human learning first The principle of easy and difficult to train the neural network significantly accelerates the convergence speed of neural network. Experimental results on AURORA2 database show that compared with baseline system, the improved method not only speeds up the training speed, but also achieves a relative performance improvement of 31.12%.

【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN912.3

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