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[Speech/Voice recognition/combine] speechcode1 DL : 0: neural network speech recognition.
Date : 2025-07-06 Size : 42kb User : jeffrey
[matlab] Using_Radial_Basis_Probabilistic_Neural_Network_f DL : 0: Using Radial Basis Probabilistic Neural Network for Speech Recognition
Date : 2025-07-06 Size : 185kb User : alamin
[Speech/Voice recognition/combine] NeuralNets DL : 0: regerding speech recognition using neural network
Date : 2025-07-06 Size : 691kb User : deepa
[AI-NN-PR] BP-network DL : 0: bp神经网络在语音识别中的应用，同时利用Matlab进行仿真得到结果-bp neural network in speech recognition applications, while the results obtained using Matlab simulation
Date : 2025-07-06 Size : 370kb User : 斯芸芸
[AI-NN-PR] abbrb DL : 0: 基于人工神经网络的语音识别，运用了从语音中提取出的LPC参数与人工神经网络结合进行识别，使用matlab调试-Artificial neural network based speech recognition, the use of speech extracted from the LPC parameters and artificial neural network to identify, debug using matlab
Date : 2025-07-06 Size : 1kb User : 屈庆琳
[Other] Pengenalan-Tutur-Terisolasi-Menggunakan-FFT-dan-J DL : 0: Isolated speech recognition using FFT for features extraction and Artificial Neural Network with Back Propagation for classificassion and recognition.
Date : 2025-07-06 Size : 146kb User : Utis Sutisna
[AI-NN-PR] Tone-Recognition DL : 0: 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．
Date : 2025-07-06 Size : 309kb User : 沈
[matlab] fangmiu_v72 DL : 0: 完整的基于HMM的语音识别系统，关于神经网络控制，采用的是通用的平面波展开法。- Complete HMM-based speech recognition system, On neural network control, Using common plane wave expansion method.
Date : 2025-07-06 Size : 5kb User : bangjieyao
[AI-NN-PR] BP DL : 0: 使用BP神经网络对语音特征信号进行分类和识别-Characteristics of speech signal using BP neural network for classification and recognition
Date : 2025-07-06 Size : 371kb User : Alian
[Other] Understanding deep learning DL : 0: Artificial intelligence (AI) is concerned with building systems that simulate intelligent behavior. It encompasses a wide range of approaches, including those based on logic, search, and probabilistic reasoning. Machine learning is a subset of AI that learns to make decisions by fitting mathematical models to observed data. This area has seen explosive growth and is now (incorrectly) almost synonymous with the term AI. A deep neural network is one type of machine learning model, and when this model is fitted to data, this is referred to as deep learning. At the time of writing, deep networks are the most powerful and practical machine learning models and are often encountered in day-to-day life. It is commonplace to translate text from another language using a natural language processing algorithm, to search the internet for images of a particular object using a computer vision system, or to converse with a digital assistant via a speech recognition interface. All of these applications are powered by deep learning. As the title suggests, this book aims to help a reader new to this field understand the principles behind deep learning. The book is neither terribly theoretical (there are no proofs) nor extremely practical (there is almost no code). The goal is to explain the underlying ideas; after consuming this volume, the reader will be able to apply deep learning to novel situations where there is no existing recipe for success. Machine learning methods can coarsely be divided into three areas: supervised, unsupervised, and reinforcement learning. At the time of writing, the cutting-edge methods in all three areas rely on deep learning (figure 1.1). This introductory chapter describes these three areas at a high level, and this taxonomy is also loosely reflected in the book’s organization.
Date : 2023-07-03 Size : 11.11mb User : ihaveap1