本文選題：人機(jī)交互　切入點(diǎn)：情感需求　出處：《復(fù)旦大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科技的迅猛發(fā)展,人們對(duì)人機(jī)交互的能力要求越來越高,對(duì)于計(jì)算機(jī)如何達(dá)到擬人化,其中最重要的一點(diǎn)就是要對(duì)人的情感信息做到準(zhǔn)確的智能識(shí)別。語音識(shí)別屬于人機(jī)交互其中一項(xiàng)重要技術(shù)能力,并且已經(jīng)成為國(guó)內(nèi)外許多學(xué)者正在探索研究的新興熱點(diǎn)問題,然而傳統(tǒng)的語音識(shí)別系統(tǒng)一般只反映了部分的信息,而忽略了語音中包含的情感信息,這樣也降低了語音識(shí)別的準(zhǔn)確率。本文將著重于對(duì)情感的聲學(xué)特征進(jìn)行研究,即通過對(duì)包含在語音中的說話人的情感特征的分析來識(shí)別說話人的情感信息,這也成為提高語音識(shí)別系統(tǒng)的識(shí)別率的重要指標(biāo)。本文在基于作者親身體驗(yàn)的基礎(chǔ)上,采用模式識(shí)別方法對(duì)上述問題作了一些基于提高識(shí)別準(zhǔn)確率的研究。首先,根據(jù)國(guó)內(nèi)外學(xué)者對(duì)情感分類的研究,結(jié)合嬰兒的心理、生理需求和所處的環(huán)境因素,將嬰兒的情感需求進(jìn)行分類定義。其次,分析目前嬰兒情感語音識(shí)別的研究現(xiàn)狀與實(shí)現(xiàn)技術(shù),分析當(dāng)前各特征參數(shù)提取的區(qū)別和意義。情感特征參數(shù)包括例如基音、能量等參數(shù),并且分析每一個(gè)特征參數(shù)和嬰兒情感特征的相關(guān)性。再次,通過分析語音識(shí)別的模式理論,包括K-近鄰方法(KNN)、隱馬爾可夫模型法(HMM)、神經(jīng)網(wǎng)絡(luò)的方法等模式識(shí)別理論對(duì)情感的模式識(shí)別可行性,提出KNN算法結(jié)合相應(yīng)情感特征參數(shù)貢獻(xiàn)率對(duì)情感語音識(shí)別的有效性。經(jīng)過綜合比較,本文將綜合多維情感特征參數(shù)和KNN算法對(duì)嬰兒的情感需求進(jìn)行模式識(shí)別實(shí)驗(yàn),并給出相應(yīng)的技術(shù)實(shí)現(xiàn)方法以及實(shí)驗(yàn)結(jié)果。
[Abstract]:With the rapid development of science and technology, people have higher and higher requirements for the ability of human-computer interaction. Among them, the most important point is to achieve accurate intelligent recognition of human emotional information. Speech recognition belongs to one of the important technical capabilities of human-computer interaction, and has become a new hot issue that many scholars at home and abroad are exploring and studying. However, the traditional speech recognition system generally only reflects part of the information, but ignores the emotional information contained in the speech, which also reduces the accuracy of speech recognition. This paper will focus on the acoustic characteristics of emotion. That is to say, the speaker's emotional information can be recognized by analyzing the emotional characteristics of the speaker included in the speech, which has become an important index to improve the recognition rate of the speech recognition system. This paper is based on the author's personal experience. Based on the research of improving the accuracy of recognition, the pattern recognition method is used to study the above problems. Firstly, according to the research of emotion classification by domestic and foreign scholars, combining the psychological and physiological needs of infants and the environmental factors, The emotional needs of infants are classified and defined. Secondly, the current research status and implementation techniques of emotional speech recognition are analyzed, and the differences and significance of extracting each feature parameter are analyzed. The emotional feature parameters include, for example, pitch, speech recognition, speech recognition, speech recognition, speech recognition, speech recognition, speech recognition, speech recognition and speech recognition. Energy and other parameters, and analyze the correlation between each characteristic parameter and the emotional characteristics of the baby. Thirdly, by analyzing the pattern theory of speech recognition, Including K-nearest neighbor method (KNNN), hidden Markov model (HMM), neural network (NN), and so on. This paper presents the validity of KNN algorithm combined with the contribution rate of corresponding emotional feature parameters to emotional speech recognition. After a comprehensive comparison, the multi-dimensional emotional feature parameters and KNN algorithm are combined to carry out pattern recognition experiments on the emotional needs of infants. The corresponding technical realization method and experimental results are also given.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN912.34

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 楊志華;齊東旭;楊力華;;一種基于Hilbert-Huang變換的基音周期檢測(cè)新方法[J];計(jì)算機(jī)學(xué)報(bào);2006年01期

，

本文編號(hào)：1628903