搜索资源列表
newpnn
- 基于概率神经网络的数字语音识别matlab程序-probabilistic neural network based on the number of voice recognition procedures Matlab
HANN
- 基于概率神经网络的数字语音识别matlab程序-probabilistic neural network based on the number of voice recognition procedures Matlab
pnn_recognition
- 基于概率神经网络方法的识别程序,主要针对语音识别,matlab程序,值得参考.-probabilistic neural network-based method of identification procedures, voice recognition, Matlab procedures, a good reference.
Great_Outdoors_by_sandals82.zi
- 一种简单有效的基于动态时变语音识别源码 对于大多数研究者来说,寻找能够匹配二重时间序列信号的最佳途径是很重要的,因为它有许多重要的应用需求.DTW是实现这项工作的显著技术,尤其在语音识别技术领域,在这里一个测试信号被按照参照模板拉伸或压缩, ,Searching for the best path that matches two time-series signals is the main task for many researchers, because of its importa
biase_bp_wave_recogin.rar
- 用BP神经网络对孤立词语音识别Matlab语音仿真过程,BP neural network used for isolated word speech recognition voice Matlab simulation
kalman
- 该程序是卡曼滤波法在语音处理上的应用,能有效的去除噪声,达到语音增强的目的!-The program is Kaman filtering method in the voice processing application, can effectively remove the noise, to achieve the purpose of speech enhancement!
speechcode1
- neural network speech recognition.
neural
- Voice recognition system using neural network
MATLAB-HMM
- HMM与小波神经网络的语音识别系统研究，此系统得到了验证-HMM and wavelet neural network speech recognition system, this system has been verified
disp_result
- matlab调用多个网络识别多种声音文件-neural network
speech-emotion-recognition
- 基于BP神经网络的语音情感识别系统 神经网络是近年来信息科学、脑科学、神经心理学等诸多学科共同关注和研究的热点。由于其具有良好的抽象分类特性,现已应用于语音识别系统的研究和开发,并成为解决识别相关问题的有效工具。文章在讲述语音识别过程的基础上重点讨论利用BP神经网络对语音进行识别,用MATLAB完成对神经网络的训练和测试,并获得满意的结果。-Based on the BP neural network speech emotion recognition system
shiyan
- 语音信号采集和频谱分析的matlab程序,对研究语音信号十分有用-Wireless sensor network localization algorithm matlab program, the study of wireless sensor network localization algorithm is useful
trainscg_10
- 利用matlab软件BP网络工具箱，画出预测语音种类和实际语音种类的分类图-BP neural network using matlab software toolbox, paint and forecast the actual voice voice types and species classification map
bp
- 基于bp神经网络，运用matlab完成识别分类的工作，自带实例-Based on BP neural network, the use of MATLAB to complete the work of identification classification, with examples