嬰兒情緒信息的模式識別技術(shù)研究與實(shí)現(xiàn)
發(fā)布時間：2019-04-16 19:24
【摘要】：嬰幼兒專家的研究成果表明,嬰兒的情緒表達(dá)不僅是與外界交流的主要方式,而且是反映其生理和心理需求、心身健康狀態(tài)乃至智力發(fā)育水平的重要信息來源。近年來,嬰兒情緒信息的研究已經(jīng)引起了人們極大的興趣并成為相關(guān)領(lǐng)域正在探索之中的新興前沿研究熱點(diǎn)。 從已有的研究工作來看,嬰兒的語音信息是最便于準(zhǔn)確采集并能體現(xiàn)嬰兒獨(dú)特的語言運(yùn)動方式和情感表達(dá)特征的重要信息,受到了研究學(xué)者們的普遍關(guān)注。然而,目前尚缺乏統(tǒng)一的嬰兒情緒分類方法及相應(yīng)的語音信息特征描述,特別是對于蘊(yùn)涵著豐富情緒信息的嬰兒笑聲和哭聲,在其內(nèi)涵意義的識別與理解上尚未形成統(tǒng)一的看法。 本文在作者親身體驗的實(shí)踐基礎(chǔ)上,采用模式識別技術(shù)對上述問題作了探索性研究。首先,從嬰兒的發(fā)生器官結(jié)構(gòu)及其情緒表達(dá)特征入手,結(jié)合嬰兒的主要生理與心理需求和其所處的環(huán)境特點(diǎn),對嬰兒的情緒狀態(tài)分類與相關(guān)的語音情緒信息作了分析。然后,通過線性預(yù)測參數(shù)(LPC)、線性預(yù)測倒譜參數(shù)(LPCC)和Mel尺度倒譜參數(shù)(MFCC)等信號分析的技術(shù)參數(shù),對嬰兒語音情緒信息的數(shù)據(jù)采集和預(yù)處理過程及相應(yīng)的特征參數(shù)提取方法進(jìn)行了研究。在此基礎(chǔ)上,本文進(jìn)一步探討和比較了人工神經(jīng)網(wǎng)絡(luò)(ANN)、隱馬爾可夫模型(HMM)、動態(tài)時間規(guī)整(DTW)等方法應(yīng)用于嬰兒情緒信息模式識別的可行性。 經(jīng)過綜合比較,本文采用了MFCC參數(shù)和DTW方法,針對嬰兒最常見的高興、饑餓、困倦三種典型的身心狀態(tài)所表達(dá)的情緒信息作模式識別研究,并給出了其技術(shù)實(shí)現(xiàn)方法和實(shí)驗測試結(jié)果,取得了良好的識別效果。本文的研究成果為相關(guān)領(lǐng)域的研究工作提供了重要的探索性啟發(fā)。
[Abstract]:The research results of infant experts show that the emotional expression of infants is not only the main way to communicate with the outside world, but also an important source of information to reflect their physiological and psychological needs, psychosomatic health status and even the level of intelligence development. In recent years, the study of infant emotional information has aroused great interest and become a new frontier research focus in related fields. According to the previous research work, the infant's speech information is the most important information which is convenient to collect accurately and can reflect the infant's unique language movement and emotion expression characteristics, which has been paid more and more attention by the researchers. However, there is still a lack of a unified classification of infant emotions and the corresponding description of the characteristics of voice information, especially for infants with abundant emotional information, including laughter and crying. The recognition and understanding of its connotation and meaning have not yet formed a unified view. On the basis of the author's personal experience, this paper makes an exploratory study of the above problems by using pattern recognition technology. Firstly, the classification of infant's emotional state and the related phonological emotional information were analyzed based on the structure of the infant's generating organ and its emotional expression characteristics, combining the main physiological and psychological needs of the infant and the environmental characteristics of the infant. Then, through the linear prediction parameter (LPC), linear prediction Cepstrum parameter (LPCC) and Mel scale cepstrum parameter (MFCC), the technical parameters of signal analysis are analyzed. The data acquisition and pre-processing process of infant speech emotion information and the corresponding feature parameters extraction method were studied. On this basis, this paper further discusses and compares the feasibility of applying artificial neural network (ANN), hidden Markov model (HMM), dynamic time regularization (DTW) to infant emotional information pattern recognition. After a comprehensive comparison, this paper adopts MFCC parameters and DTW method to study the emotional information expressed in three typical physical and mental states of infants: happy, hungry and sleepy, and makes a pattern recognition study on the emotional information of the three typical physical and mental states of the infant, namely, happy, hungry and sleepy. The technical realization method and experimental test results are given, and good recognition results are obtained. The research results of this paper provide an important exploratory inspiration for the research work in related fields.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R174;TN912.34
本文編號：2459032
[Abstract]:The research results of infant experts show that the emotional expression of infants is not only the main way to communicate with the outside world, but also an important source of information to reflect their physiological and psychological needs, psychosomatic health status and even the level of intelligence development. In recent years, the study of infant emotional information has aroused great interest and become a new frontier research focus in related fields. According to the previous research work, the infant's speech information is the most important information which is convenient to collect accurately and can reflect the infant's unique language movement and emotion expression characteristics, which has been paid more and more attention by the researchers. However, there is still a lack of a unified classification of infant emotions and the corresponding description of the characteristics of voice information, especially for infants with abundant emotional information, including laughter and crying. The recognition and understanding of its connotation and meaning have not yet formed a unified view. On the basis of the author's personal experience, this paper makes an exploratory study of the above problems by using pattern recognition technology. Firstly, the classification of infant's emotional state and the related phonological emotional information were analyzed based on the structure of the infant's generating organ and its emotional expression characteristics, combining the main physiological and psychological needs of the infant and the environmental characteristics of the infant. Then, through the linear prediction parameter (LPC), linear prediction Cepstrum parameter (LPCC) and Mel scale cepstrum parameter (MFCC), the technical parameters of signal analysis are analyzed. The data acquisition and pre-processing process of infant speech emotion information and the corresponding feature parameters extraction method were studied. On this basis, this paper further discusses and compares the feasibility of applying artificial neural network (ANN), hidden Markov model (HMM), dynamic time regularization (DTW) to infant emotional information pattern recognition. After a comprehensive comparison, this paper adopts MFCC parameters and DTW method to study the emotional information expressed in three typical physical and mental states of infants: happy, hungry and sleepy, and makes a pattern recognition study on the emotional information of the three typical physical and mental states of the infant, namely, happy, hungry and sleepy. The technical realization method and experimental test results are given, and good recognition results are obtained. The research results of this paper provide an important exploratory inspiration for the research work in related fields.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R174;TN912.34
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