Description: 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 Platform: |
Size: 1024 |
Author:屈庆琳 |
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Description: Isolated speech recognition using FFT for features extraction and Artificial Neural Network with Back Propagation for classificassion and recognition. Platform: |
Size: 149504 |
Author:Utis Sutisna |
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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: 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 Platform: |
Size: 379904 |
Author:Alian |
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Description: 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. Platform: |
Size: 11646296 |
Author:ihaveap1 |
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