基于ARM的嵌入式语音识别系统研究

在分析语音识别原理的基础上,设计了一个基于ARM9和嵌入式Linux的嵌入式语音识别系统。采用动态时间归整(DTW)算法对语音信号进行特征参数序列比较并识别出结果。采用S3C2410微处理器和嵌入式Linux操作系统,将交叉编译后的语音识别C语言程序编译进嵌入式Linux操作系统的文件系统,实现语音识别系统的功能。     

嵌入式小词汇量语音识别系统

介绍了一种基于嵌入式微处理器的小词汇量语音识别子系统的具体实现,包括软、硬件环境以及在嵌入式语音识别环境下采取的特殊策略。基于嵌入式微处理器的系统能够实现较复杂的控制,并且具有较好的可扩展性。     

嵌入式语音识别系统性能分析

语音识别技术在嵌入式系统上的应用是当前的热点和难点。本文在三种不同的嵌入式系统上建立了基于HMM的非特定人大词汇表连续语音识别的实验平台,对语音识别的实时性能进行了测试分析,讨论了非特定人连续语音识别系统在不同嵌入式平台上的可行性。     

基于小波神经网络的嵌入式语音识别系统

本文介绍了基于嵌入式操作系统Windows CE和ARM平台的语音识别系统,该系统使用了小波神经网络技术。系统使用S3C2410芯片进行控制和语音识别,使用SPCE061A芯片完成训练算法、语音信号特征提取,具有较好的可移植性,在小波神经网络算法的帮助下,系统有较高的识别率。     

基于嵌入式Linux语音识别系统的设计

该设计运用三星公司的S3C2440,结合ICRoute公司的高性能语音识别芯片LD3320,进行了语音识别系统的硬件和软件设计。在嵌入式Linux操作系统下,运用多进程机制完成了对语音识别芯片、超声波测距和云台的控制,并将语音识别技术应用于多角度超声波测距系统中。通过测试,系统可以通过识别语音指令控制测量方向,无需手动干预,最后将测量结果通过语音播放出来。     

嵌入式语音识别系统研究

对嵌入式语音识别系统所涉及的硬件、外围接口电路作了分析比较,重点分析了S3C2410和存储模块;对嵌入式操作系统Windows CE和Linux作了介绍,重点分析了Linux及其引导程序BootLoader;对嵌入式系统识别算法的选取作了分析研究。     

LD3320的嵌入式语音识别系统的应用

语音交互系统是比较人性化的人机操作界面,它需要语音识别系统的支持。LD3320就是这样一款语音识别芯片。介绍了该芯片的工作原理及应用,给出了LD3320与微处理器的硬件接口电路及软件程序。随着高档MCU的不断出现,以MCU为核心的嵌入式语音交互系统会有非常好的应用前景。     

基于DSP的嵌入式语音识别模块的实现

设计并实现了一个特定人、孤立词、小词汇量的嵌入式语音识别模块,该模块能够方便地与多种控制芯片接口从而实现不同的功能.在硬件上,使用高速的TMS320VC5402芯片作为模块的运算单元;在软件上,采用谱减法进行前端去噪处理,接着根据谱熵进行语音端点检测,然后提取12维MFCC及其一阶差分作为特征参数,最后用加入模板阈值方法的改进型DTW算法完成整个识别过程.实验结果证明,该语音识别模块在满足实时性的前提下具有良好的识别率和抗干扰性能.     

基于DSP的非特定人语音识别系统

本文介绍了一种基于TMS320C6711 DSP的非特定人、孤立词语音识别系统。本文首先介绍了语音识别技术的基本原理,然后对不同的识别算法在多种嵌入式系统平台上进行性能分析和比较,可得到本语音识别系统具有较高的识别率、实时性和鲁棒性。     

嵌入式语音识别系统的研究和实现

本文首先给出了一种适合于在嵌入式平台上实现的可变命令集的非特定人语音识别系统,同传统的基于PC的非特定人语音识别系统相比,该系统具备内存消耗小,运算速度快的优点。然后给出了该语音识别系统在多种嵌入式平台上的实现和评估结果,论证了非特定人语音识别系统在嵌入式平台上实现的可行性及其对硬件的最低配置要求,在技术层次上分析了目前实现高性能语音识别SOC的主要问题和困难,并指出了今后相关的研究方向。     

基于深度学习的嵌入式离线语音识别系统设计

基于深度学习库Tensorflow和深度可分离卷积神经网络(Depthwise Separable Convolutional Neural Network,DS-CNN),实现一个嵌入式离线语音识别系统。利用Tensorflow和DS-CNN对预识别语音进行训练得到声学模型,移植该声学模型至嵌入式处理器中;对采集编码的语音信号经过分帧、加窗等预处理,采用梅尔频率倒谱系数(Mel Frequency Cepstral Coefficent,MFCC)方法进行特征提取,利用声学模型对提取的特征进行分类判别。测试结果表明,基于深度学习的语音识别可以有效地应用在嵌入式平台上,相比于一些传统算法,在识别率和识别时间上有明显的提高。     

基音周期标准化与嵌入式语音识别系统

根据不同人发相同音节时,一个基音周期内的波形具有一定相似性的特点,提出一种新的基音周期标准化的语音信号预处理方法。该方法在一个嵌入式的、非特定人、孤立数字的语音识别系统中进行了验证,实验结果表明基音周期标准化能有效提高语音识别的准确率。     

基于HMM的维吾尔语连续语音识别系统

维吾尔语是黏着性语言,利用丰富的词缀可以用同样的词干产生超大词汇,给维吾尔语语音识别的研究工作带来了很大困难。结合维吾尔语自身特点,建立了维吾尔语连续语音语料库,利用HTK（HMMToolKit）工具实现了基于隐马尔可夫模型（HMM）的维吾尔语连续语音识别系统。在声学层,选取三音子作为基本的识别单元,建立了维吾尔语的三音子声学模型,并使用决策树、三音子绑定、修补哑音、增加高斯混合分量等方法提高模型的识别精度。在语言层,使用了适合于维吾尔语语音特征的基于统计的二元文法语言模型。最后,利用该系统进行了维吾尔语连续语音识别实验。     

An embedded system for real-time facial expression recognition based on the extension theory

This paper presents a novel facial expression recognition scheme based on extension theory. The facial region is detected and segmented by using feature invariant approaches. Accurate positions of the lips are then extracted as the features of a face. Next, based on the extension theory, basic facial expressions are classified by evaluating the correlation functions among various lip types and positions of the corners of the mouth. Additionally, the proposed algorithm is implemented using the XScale PXA270 embedded system in order to achieve real-time recognition for various facial expressions. Experimental results demonstrate that the proposed scheme can recognize facial expressions precisely and efficiently.     

An efficient static gesture recognizer embedded system based on ELM pattern recognition algorithm

Millions of people throughout the world describe themselves as being deaf. Some of them suffer from severe hearing loss and consequently use an alternative manner with which to communicate with society by means of either written or visual language. There are several sign languages capable of dealing with such a need. Nonetheless, a communication gap still exists even when using such languages, since only a small fraction of the population is able to use them. Over the last few years, due to the increasing need for universal accessibility when using computational resources, gesture recognition has been widely researched. Thus, in an attempt to reduce this communication gap, our approach proposes a computational solution in order to translate static gesture symbols into text symbols, through computer vision, without the use of hand sensors or gloves. In order to guarantee the highest quality, with emphasis on the reliability of the system and real-time translation, we have developed an approach based on the Extreme Learning Machine (ELM) pattern recognition algorithms fully implemented in hardware, and have assessed it to measure these two metrics. Hardware components were designed in order to perform the best image processing and pattern recognition tasks used within the project. As a case study, and so as to validate the technique, a recognition system for the Brazilian Sign Language (LIBRAS) was implemented. Besides ensuring that this approach could be used for any static hand gesture symbol recognition, our main goal was to guarantee fast, reliable gesture recognition for communication between humans. Experimental results have demonstrated that the system is able to recognize LIBRAS symbols with an accuracy of 97%, a response time of 6.5ms per letter recognition, and using only 43% (about 64,851 logic elements) of the FPGA area.     

嵌入式实时音乐语音识别系统的实现

设计并实现了一种嵌入式实时音乐语音识别系统.叙述了音乐语音识别系统硬件结构、软件流程,建立了一种基于多频段能量曲线分割结合过零率来检测端点的新方法,实验结果表明,该系统对特定人的平均识别率在96%以上.     

An automatic speech recognition system for spontaneous Punjabi speech corpus

Automatic speech recognition is the central part of the wheel towards the natural person-to-machine interaction technique. Due to the high disparity of speaking styles, speech recognition surely demands composite methods to constitute this irregularity. A speech recognition method can work in numerous distinct states such as speaker dependent/independent speech, isolated/continuous/spontaneous speech recognition, for less to very large vocabulary. The Punjabi language is being spoken by concerning 104 million peoples in India, Pakistan and other countries with Punjabi migrants. The Punjabi language is written in Gurmukhi writing in Indian Punjab, while in Shahmukhi writing in Pakistani Punjab. In the paper, the objective is to build the speaker independent automatic spontaneous speech recognition system for the Punjabi language. The system is also capable to recognize the spontaneous Punjabi live speech. So far, no work has to be achieved in the area of spontaneous speech recognition system for the Punjabi language. The user interfaces for Punjabi live speech system is created by using the java programming. Till now, automatic speech system is trained with 6012 Punjabi words and 1433 Punjabi sentences. The performance measured in terms of recognition accuracy which is 93.79% for Punjabi words and 90.8% for Punjabi sentences.     

一种基于嵌入式系统实时交互的手势识别方法*

提出了一种在单摄像头条件下基于嵌入式系统的手势识别方法。通过拟合手势图的外接多边形,找出其所对应的手势缺陷图,并建立手势与手势缺陷图的一一映射,利用手势缺陷图的特征来匹配和识别不同的手势。算法还将手势的跟踪与识别有机地统一起来,通过预测下一帧中手势出现的粗略位置大大降低识别步骤的计算量。该算法在实际应用的嵌入式平台下,能快速、准确地实现手势的识别,能够满足实时人机交互的要求。