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Improved Speech Recognition Method for Intelligent Robot

Improved Speech Recognition Method for Intelligent Robot
Improved Speech Recognition Method for Intelligent Robot

附录一:英文技术资料翻译

英文原文:

Improved Speech Recognition Method for Intelligent Robot

1、Overview of speech recognition

Speech recognition has received more and more attention recently due to the important theoretical meaning and practical value. Up to now, most speech recognition is based on conventional linear system theory, such as Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). With the deep study of speech recognition, it is found that speech signal is a complex nonlinear process. If the study of speech recognition wants to break through, nonlinear-system theory method must be introduced to it. Recently, with the development of nonlinear-system theories such as artificial neural networks (ANN), chaos and fractal, it is possible to apply these theories to speech recognition. Therefore, the study of this paper is based on ANN and chaos and fractal theories are introduced to process speech recognition.

Speech recognition is divided into two ways that are speaker dependent and speaker independent. Speaker dependent refers to the pronunciation model trained by a single person, the identification rate of the training person’ orders are high, while others’ orders are in low identification rate or can’t be recognized. Speaker independent refers to the pronunciation model trained by persons of different age, sex and region, it can identify a group of persons’ orders. Generally, speaker independent system is more widely used, since the user is not required to conduct the training. So extraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system.

Speech recognition can be viewed as a pattern recognition task, which includes training and recognition. Generally, speech signal can be viewed as a time sequence and characterized by the powerful hidden Markov model (HMM). Through the feature extraction, the speech signal is transferred into feature vectors and act as observations. In the training procedure, these observations will feed to estimate the model parameters of HMM. These parameters include probability density function for the observations and their corresponding states, transition probability between the states, etc. After the parameter estimation, the trained models can be used for recognition task. The input observations will be recognized as the resulted words and the accuracy can be evaluated. The whole process is illustrated in Fig. 1.

Fig. 1Block diagram of speech recognition system

2、Theory and method

Extraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system. The standard methodology for solving this problem uses Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Co-efficient (MFCC). Both these methods are linear procedures based on the assumption that speaker features have properties caused by the vocal tract resonances. These features form the basic spectral structure of the speech signal. However, the non-linear information in speech signals is not easily extracted by the present feature extraction methodologies. So we use fractal dimension to measure non2linear speech turbulence.

This paper investigates and implements speaker identification system using both traditional LPCC and non-linear multiscaled fractal dimension feature extraction.

2. 1Linear Predictive Cepstral Coefficients

Linear prediction coefficient (LPC) is a parameter set which is obtained when we do linear prediction analysis of speech. It is about some correlation characteristics between adjacent speech samples. Linear prediction analysis is based on the following basic concepts. That is, a speech sample can be estimated approximately by the linear combination of some past speech samples. According to the minimal square sum principle of difference between real speech sample in certain analysis frame short-time and predictive sample, the only group of prediction coefficients can be determined.

LPC coefficient can be used to estimate speech signal cepstrum. This is a special processing method in analysis of speech signal short-time cepstrum. System function of channel model is obtained by linear prediction analysis as follow.

Where p represents linear prediction order, a k,(k=1,2,…,p) represent sprediction coefficient, Impulse response is represented by h(n). Suppose cepstrum of h(n) is

represented by ,then (1) can be expanded as (2).

The cepstrum coefficient calculated in the way of (5) is called LPCC, n represents LPCC order.

When we extract LPCC parameter before, we should carry on speech signal pre-emphasis, framing processing, windowing processing and endpoints detection etc. , so the endpoint detection of Chinese command word “Forward”is shown in Fig.2, next, the speech waveform of Chinese command word “Forward” and LPCC parameter waveform after Endpoint detection is shown in Fig. 3.

2. 2 Speech Fractal Dimension Computation

Fractal dimension is a quantitative value from the scale relation on the meaning of fractal, and also a measuring on self-similarity of its structure. The fractal measuring is fractal dimension. From the viewpoint of measuring, fractal dimension is extended from integer to fraction, breaking the limit of the general to apology set dimension being integer Fractal dimension, fraction mostly, is dimension extension in Euclidean geometry.

There are many definitions on fractal dimension, eg.,similar dimension, Hulshof dimension, info ration dimension, correlation dimension, capability immersion, box-counting dimension etc. , where, Hausdoff dimension is oldest and also most important, for any sets, it is defined as[3].

Where, M£(F) denotes how many unit £needed to cover subset F.

In thispaper, the Box-Counting dimension (DB) of ,F, is obtained by partitioning the plane with squares grids of side £, and the numberof squares that intersect the plane (N(£)) and is defined as[8].

The speech waveform of Chinese command word “Forward”and fractal dimension waveform after Endpoint detection is shown in Fig. 4.

2. 3Improved feature extractions method

Considering the respective advantages on expressing speech signal of LPCC and fractal dimension, we mix both to be the feature signal, that is, fractal dimension denotes the self2similarity, periodicity and randomness of speech time wave shape, meanwhile LPCC feature is good for speech quality and high on identification rate.

Due to ANN′s nonlinearity, self-adaptability, robust and self-learning such obvious advantages, its good classification and input2output reflection ability are suitable to resolve speech recognition problem.

Due to the number of ANN input nodes being fixed, therefore time regularization is carried out to the feature parameter before inputted to the neural network[9]. In our experiments, LPCC and fractal dimension of each sample are need to get through the network of time regularization separately, LPCC is 4-frame data(LPCC1,LPCC2,LPCC3,LPCC4, each frame parameter is 14-D), fractal dimension is regularized to be12-frame data(FD1,FD2,…,FD12, each frame parameter is 1-D), so that the feature vector of each sample has 4*14+1*12=68-D, the order is, the first 56 dimensions are LPCC, the rest 12 dimensions are fractal dimensions. Thus, such mixed feature parameter can show speech linear and nonlinear characteristics as well.

中文译文:

改进型智能机器人的语音识别方法

1、语音识别概述

近来,语音识别由于其重大的理论意义和实用价值已经受到越来越多的关注。到现在为止,多数的语音识别是基于传统的线性系统理论,例如隐马尔可夫模型和动态时间规整技术。随着语音识别的深度研究,研究者发现,语音信号是一个复杂的非线性过程,如果语音识别研究想要获得突破,那么就必须引进非线性系统理论方法。最近,随着非线性系统理论的发展,如人工神经网络,混沌与分形,可能应用这些理论到语音识别中。因此,本文的研究是在神经网络和混沌与分形理论的基础上介绍了语音识别的过程。

语音识别可以划分为独立发声式和非独立发声式两种。非独立发声式是指发音模式是由单个人来进行训练,其对训练人命令的识别速度很快,但它对与其他人的指令识别速度很慢,或者不能识别。独立发声式是指其发音模式是由不同年龄,不同性别,不同地域的人来进行训练,它能识别一个群体的指令。一般地,由于用户不需要操作训练,独立发声式系统得到了更广泛的应用。所以,在独立发声式系统中,从语音信号中提取语音特征是语音识别系统的一个基本问题。

语音识别包括训练和识别,我们可以把它看做一种模式化的识别任务。通常地,语音信号可以看作为一段通过隐马尔可夫模型来表征的时间序列。通过这些特征提取,语音信号被转化为特征向量并把它作为一种意见,在训练程序中,这些意见将反馈到HMM的模型参数估计中。这些参数包括意见和他们响应状态所对应的概率密度函数,状态间的转移概率,等等。经过参数估计以后,这个已训练模式就可以应用到识别任务当中。输入信号将会被确认为造成词,其精确度是可以评估的。整个过程如图一所示。

图1 语音识别系统的模块图

2、理论与方法

从语音信号中进行独立扬声器的特征提取是语音识别系统中的一个基本问题。解决这个问题的最流行方法是应用线性预测倒谱系数和Mel频率倒谱系数。这两种方法都是基于一种假设的线形程序,该假设认为说话者所拥有的语音特性是由于声道共振造成的。这些信号特征构成了语音信号最基本的光谱结构。然而,在语音信号中,这些非线形信息不容易被当前的特征提取逻辑方法所提取,所以我们使用分型维数来测量非线形语音扰动。

本文利用传统的LPCC和非线性多尺度分形维数特征提取研究并实现语音识别系统。

2.1 线性预测倒谱系数

线性预测系数是一个我们在做语音的线形预分析时得到的参数,它是关于毗邻语音样本间特征联系的参数。线形预分析正式基于以下几个概念建立起来的,即一个语音样本可以通过一些以前的样本的线形组合来快速地估计,根据真实语音样本在确切的分析框架(短时间内的)和预测样本之间的差别的最小平方原则,最后会确认出唯一的一组预测系数。

LPC可以用来估计语音信号的倒谱。在语音信号的短时倒谱分析中,这是一种特殊的处理方法。信道模型的系统函数可以通过如下的线形预分析来得到:

其中p代表线形预测命令,,(k=1,2,… …,p)代表预测参数,脉冲

响应用h(n)来表示,假设h(n)的倒谱是。那么(1)式可以扩展为(2)式:

将(1)带入(2),两边同时,(2)变成(3)。

就获得了方程(4):

那么可以通过来获得。

(5)中计算的倒谱系数叫做LPCC,n代表LPCC命令。

在我们采集LPCC参数以前,我们应该对语音信号进行预加重,帧处理,加工和终端窗口检测等,所以,中文命令字“前进”的端点检测如图2所示,接下来,断点检测后的中文命令字“前进”语音波形和LPCC的参数波形如图3所示。

图2 中文命令字“前进”的端点检测

图3 断点检测后的中文命令字“前进”语音波形和LPCC的参数波形

2.2 语音分形维数计算

分形维数是一个与分形的规模与数量相关的定值,也是对自我的结构相似性的测量。分形分维测量是[6-7]。从测量的角度来看,分形维数从整数扩展到了分数,打破了一般集拓扑学方面被整数分形维数的限制,分数大多是在欧几里得几何尺寸的延伸。

有许多关于分形维数的定义,例如相似维度,豪斯多夫维度,信息维度,相关维度,容积维度,计盒维度等等,其中,豪斯多夫维度是最古老同时也是最重要的,它的定义如【3】所示:

其中,表示需要多少个单位来覆盖子集F.

端点检测后,中文命令词“向前”的语音波形和分形维数波形如图4所示。

图4 端点检测后,中文命令词“向前”的语音波形和分形维数波形

2.3 改进的特征提取方法

考虑到LPCC语音信号和分形维数在表达上各自的优点,我们把它们二者混合到信号的特取中,即分形维数表表征语音时间波形图的自相似性,周期性,随机性,同时,LPCC特性在高语音质量和高识别速度上做得很好。

由于人工神经网络的非线性,自适应性,强大的自学能力这些明显的优点,它的优良分类和输入输出响应能力都使它非常适合解决语音识别问题。

由于人工神经网络的输入码的数量是固定的,因此,现在是进行正规化的特征参数输入到前神经网络[9],在我们的实验中,LPCC和每个样本的分形维数需要分别地通过时间规整化的网络,LPCC是一个4帧数据(LPCC1,LPCC2,LPCC3,LPCC4,每个参数都是14维的),分形维数被模范化为12维数据,(FD1,FD2,…FD12,每一个参数都是一维),以便于每个样本的特征向量有4*14+12*1=68-D维,该命令就是前56个维数是LPCC,剩下的12个维数是分形维数。因而,这样的一个特征向量可以表征语音信号的线形和非线性特征。

最新合肥工业大学研究生英语复习课本重点句

1.I have never cultivated a mustache though I’m sure one would enhance my distinguished looks and cause women to giggle as I passed along the boulevard. 尽管我确信蓄胡子会使我更加气度不凡,走在大街上会使女性发笑,但我从不留胡子。 2.I might be thrown into such a panic that I’d blurt out ... 我可能会惊慌得脱口而出... 3.It is one of the paradoxes of social intercourse that a compliment is harder to respond to than an insult. Here is an area of small talk that most of us act awkwardly. 在社会交往中,应对恭维比对付辱骂要艰难得多,这话听起来有点矛盾,却有一定的道理。闲聊时来句恭维话,往往让我们大多数人不知所措。 5.Someone utters a pleasing, praiseful remark in our direction and we grow inarticulate and our kneecaps begin to vibrate. 有人对我们说上一句动听、赞美的话,我们就慌得说不出话来,膝盖开始瑟瑟发抖。 6.I can’t accept with grace a compliment bestowed upon me for a thing that isn’t real ly mine. 如果别人称赞不是真正属于我自己的东西时,我根本无法欣然接受. 7.The nearest I ever came to downright acceptance of this particular compliment was the time I said, “Well, we like it.” 我在接受这种特定的恭维时,表示最能完全接受的说法就是“嗯,我们喜欢。” 8.... carried away by the vastness of his complimentary remark ... 被他的这种极度夸张的恭维话所吸引的 9.I think we make a mistake when we react to a compliment with denial and derogation. 我认为,对待恭维采取否定和贬低的态度是错误的。 10.The situation here is much the same as the one regarding my view. 这种情景,与我上述提出的观点非常相似。 11.I know a man who has put his mind to this problem and come up with a technique for brushing off praise. 我认识一个潜心研究这种问题的人,他想出了一个办法来避开别人的表扬。 12.He employs a sort of unreasonable realism. 他采取了一种不近情理的现实态度。 13.I don’t think this fellow is on the right track. 我想这个家伙回答的方式有问题。 14.This sort of thing, the witty reply, ought to be placed under government regulation. 这种俏皮机智的应答,应该置于政府的规定之中。 15.That one, I thought, was more than passable. 我想,这个回答相当不错。 16.But for every genuinely clever retort there are a thousand that fall flat. 但是,在千百次的应对中才会有一句真正巧妙的应答。 17.It takes a Dorothy Parker or a George S. Kaufman to handle the quip comeback with skill. 只有像多萝西·帕克或乔治·考夫曼这样的人才能应对自如。 18.… swell out their chests…挺着胸脯 19.I worked like a dog to get it written. 我当时写得好苦啊。 20.... the unwritten code of authorhood ... 著书人的一条不成文的规定. 21.... with immeasurably lovely egg on your face ... 一脸尴尬 22.There is no point in trying to play the game back at them —they’ll top you in the end, no matter what. 要想回敬他们是没有用的——不管说什么,最后他们总会占上风。 23.... at the bottom of all graceful social intercourse lies poise ... 在所有得体的社交场合,最根本的就是保持镇定24.Given the same circumstances I would have quietly asked for a coil of rope. 要是我遇到这种情况,我会感到极为窘迫,恨不得悄悄地找根绳子去上吊。 . 25.If we could all comport ourselves with that kind of dignity, and quit jittering, our social life would be much more enjoyable. 要是我们的行为举止都能保持这种风度,摆脱局促不安,那我们的社交生活就会有趣得多。 26.... the chances he’s just making conversation ...他可能只是想和你说说话。 Unit 2 1. I date a woman for a while—literary type, well-read, lots of books in her place—whom I admired a bit too extravagantly

合工大研究生英语翻译

汉翻英第一单元 1、要善于恭维他人,重要的一步就是要懂得为什么恭维会有助于你建立更好的人际关系。 An important step in becoming an effective flatterer is to understand why flattery helps you establish better relationships with others. 2、恭维之所以奏效,最根本的原因是恭维符合了人类行为的一个基本原则:人们渴望得到赏识。 The root cause of the power of flattery gets at(达到) a basic principle of human behavior:People crave being appreciated. Power of flattery lies in the fact that it fulfils a basic principle of human behavior: 3、尽管文化背景各不相同,但绝大多数人都有类似的想法。 The vast majority of people are of the similar idea despite different cultures. 4、在亚洲文化中,人们对群体赏识的渴求一般要强于对个体赏识的渴求。但不管怎样,人们渴望赏识是普遍存在的。 In Asian cultures the desire for group recognition is generally stronger than the desire for individual recognition. Nevertheless, the need for recognition is present/universal. 5、很多人认为,工作本身带来的乐趣要比外界赏识包括恭维更为重要。Many people hold that the joy of work itself is more important than external recognition, including flattery. 6、工作的乐趣也许是一种巨大的动力,但是即使是那些从工作中得到极大乐趣的人如科学家、艺术家、摄影师也渴望得到恭维和认可,否则他们就不会去竞争诺贝尔奖或在重要的展览会上展示他们的作品了。 The joy of work may be a powerful motivator, but even those who get the biggest joy from their work —such as scientists, artists, and photographers —crave flattery and recognition. 7、否则他们就不会去竞争诺贝尔奖或在重要的展览会上展示他们的作品了。Otherwise they wouldn’t compete for Nobel Prizes or enter their work in important exhibitions. 8、恭维之所以奏效,还因为它与人们对认可的正常需要有关。 Another reason flattery is so effective relates to the normal need to be recognized. 9、尽管有一些关于恭维的书和文章问世,并对恭维极力进行宣扬,但是大多数人还是没有得到应有的赏识。 Although some articles and books have been written and preached zealously about flattery, most people receive less recognition than they deserve. 10、很多人无论在工作上或在家里都很少收到赞美,所以对认可的渴求就更加强烈了。 Many people hardly ever receive compliments either on the job or at home, thus intensifying their demand for flattery. 汉翻英第二单元 1、鲜花是最常送的礼物之一。 Flowers are among the most frequently given gifts. 2、有一种传统的用鲜花表达的语言。精心挑选的一束花卉可以传达多种不同的情感和祝福。 There’s a traditional floral language, and a carefully selected bouquet or plant can convey a wide range of emotions (love, affection, pity)and sentiments 、 3、红玫瑰象征着爱情也象征着新事业充满希望的开端; Red roses symbolize love as well as the hopeful beginning of a new enterprise; 4、紫罗兰是祈求受花人不要忘却送花人。 violets beseech the recipient not to forget the donor; 5、兰花以及其他精美的花卉则表示(你希望)受花人认为你情调高雅(decent, graceful, elegant)、受人尊重(esteemed, respectable)、出类拔萃(outstanding, distinguished, celebrated)。 orchids and other exquisite blooms indicate that the recipient regards you as exotic(珍奇的), precious(讲究的)and rare(珍贵的). 6、送一束鲜花能唤起温馨的回忆,比那些仅仅显示炫耀和奢华的礼物更为珍贵。 A floral gift that evokes warm recollections will be prized more than one that is

合工大研究生英语课后汉翻英中英对照版考试必备(精)

要善于恭维他人,重要的一步就是要懂得为什么恭维会有助于你建立更好的人际关系。 An important step in becoming an effective flatterer is to understand why flattery helps you establish better relationships with others. 恭维之所以奏效,最根本的原因是恭维符合了人类行为的一个基本原则:人们渴望得到赏识。 The root cause of the power of flattery gets at a basic principle of human behavior: People crave being appreciated., 尽管文化背景各不相同,但绝大多数人都有类似的想法。 he vast majority of people are of the similar idea despite different cultures. 在亚洲文化中,人们对群体赏识的渴求一般要强于对个体赏识的渴求。 In Asian cultures the desire for group recognition is generally stronger than the desire for individual recognition. 但不管怎样,人们赏识是普遍存在的。 Nevertheless, the need for recognition is present. 很多人认为,工作本身带来的乐趣要比外界赏识包括恭维更为重要。工作的乐趣也许是一种巨大的动力,但是即使是那些从工作中得到极大乐趣的人,如科学家、艺术家、摄影师也渴望得到恭维和认可,否则他们就不会去竞争诺贝尔或在重要的展览会上展示他们的作品了。 Many people hold that the joy of work itself is more important than external recognition, including flattery. The joy of work may be a powerful motivator, but even those who get the biggest joy from their work--- such as scientists, artists, and photographers --- crave flattery and recognition. Otherwise they wouldn’t compete for Nobel Prizes or enter their work in important exhibitions.

2017合工大研究生英语全缩印版

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