Description: 基于GMM的概率神经网络PNN具有良好的泛化能力，快速的学习能力，易于在线更新,并具有统计学的贝叶斯估计理论基础，已成为一种解决像说话人识别、文字识别、医疗图像识别、卫星云图识别等许多实际困难分类问题的很有效的工具。而且PNN不但具有GMM的大部分优点，还具有许多GMM没有的优点，如强鲁棒性，需要更少的训练语料，可以和其他网络其他理论无缝整合等。-GMM based probabilistic neural network PNN good generalization ability, the ability to learn fast, easy online updates, and with the Bayesian statistical theory based on estimates, and has become a solution as speaker recognition, text recognition, medical image recognition, satellite images and other real recognition when difficulties classification of very effective tool. But GMM PNN is not only the most advantages, but also has many advantages GMM not as strong robustness, require less training corpus, and other networks to other theories, such as seamless integration. Platform: |
Size: 7158 |
Author:姜正茂 |
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Description: 用三层小波神经网络实现的与文本无关说话人识别。（识别部分）。输入的是语音特征，输出的是识别结果。训练用的语音特征要事先提取出。-with three wavelet neural network has nothing to do with the text of Speaker Recognition. (Recognition). The admission of voice features, the output is the result of recognition. Training voice features prior to extract. Platform: |
Size: 6193 |
Author:白莹 |
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Description: 用三层小波神经网络实现的与文本无关说话人识别。（识别部分）。输入的是语音特征，输出的是识别结果。训练用的语音特征要事先提取出。-with three wavelet neural network has nothing to do with the text of Speaker Recognition. (Recognition). The admission of voice features, the output is the result of recognition. Training voice features prior to extract. Platform: |
Size: 6144 |
Author:白莹 |
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Description: 基于GMM的概率神经网络PNN具有良好的泛化能力，快速的学习能力，易于在线更新,并具有统计学的贝叶斯估计理论基础，已成为一种解决像说话人识别、文字识别、医疗图像识别、卫星云图识别等许多实际困难分类问题的很有效的工具。而且PNN不但具有GMM的大部分优点，还具有许多GMM没有的优点，如强鲁棒性，需要更少的训练语料，可以和其他网络其他理论无缝整合等。-GMM based probabilistic neural network PNN good generalization ability, the ability to learn fast, easy online updates, and with the Bayesian statistical theory based on estimates, and has become a solution as speaker recognition, text recognition, medical image recognition, satellite images and other real recognition when difficulties classification of very effective tool. But GMM PNN is not only the most advantages, but also has many advantages GMM not as strong robustness, require less training corpus, and other networks to other theories, such as seamless integration. Platform: |
Size: 7168 |
Author:姜正茂 |
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Description: 基于小波神经网络PNN 的说话人识别程序，希望对大家有所帮助-Based on Wavelet Neural Network PNN the speaker recognition process, and they hope to help everyone Platform: |
Size: 7168 |
Author:张杰 |
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Description: 常用的说话人识别方法有模板匹配法、统计建模法、联接主义法(即人工神经网络实现)。考虑到数据量、实时性以及识别率的问题，采用基于矢量量化和隐马尔可夫模型(HMM)相结合的方法。
说话人识别的系统主要由语音特征矢量提取单元(前端处理)、训练单元、识别单元和后处理单元组成，
-Commonly used methods of speaker recognition template matching method, statistical modeling method, and connection method (ie, artificial neural networks). Taking into account the amount of data, real-time as well as the recognition rate of the problem, based on vector quantization and Hidden Markov Model (HMM) method of combining. Speaker recognition system mainly by the voice feature vector extraction unit (front-end treatment), training modules, identification and post-treatment unit modules, Platform: |
Size: 64512 |
Author:孙丽 |
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Description: 调信息在汉语语音识别中具有非常重要的意义。采用支持向量机对连续汉语连续语音进行声调识别实
验，首先采用基于Teager能量算子和过零率的两级判别策略对连续语音进行浊音段提取，然后建立了适合于支持向
量机分类模型的等维声调特征向量。使用6个二类SVM模型对非特定人汉语普通话的4种声调进行分类识别，与
BP神经网络相比，支持向量杌具有更高的识别率。-Tone is an essential component for word formation in Chinese languages．It plays a very important role in the
transmission of information in speech communication．We looked at using support vector machines(SVMs)for auto—
matic tone recognition in continuously spoken Mandarin．The voiced segments were detected based on Teager Energy
Operation and ZCIL Compared with BP neural network。considerable improvement was achieved by adopting 6 binary-
SVMs scheme in a speaker-independent Mandarin tone recognition system． Platform: |
Size: 316416 |
Author:沈 |
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Description: 语音信号处理__赵力。共分十二章，内容包括：绪论、语音信号处理的基础知识、语音信号的分析技术、语音信号的矢量量化、隐马尔可夫模型技术、神经网络在语音信号处理中的应用、语音编码、语音合成、语音识别、说话人识别和语种辨识技术、语音信号的情感信息处理技术、语音增强技术-Voice signal processing __ Zhao. Divided into 12 chapters, including: the introduction, the basics of voice signal processing, speech signal analysis techniques, the speech signal vector quantization and hidden Markov model, neural network applications in speech signal processing, speech coding, speech synthesis, speech recognition, speaker recognition and language identification technology, the voice signal emotional information processing technology, speech enhancement technology Platform: |
Size: 9973760 |
Author:luocw138 |
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