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[Speech/Voice recognition/combine] Speech.Recognition.using.Neural.Networks DL : 0: Tutorial for speech recognition
Update : 2025-02-19 Size : 971kb Publisher : suigintouml
[Industry research] Speech_Recognition_using_Neural_Networks DL : 0: Speech Recognition using Neural Networks
Update : 2025-02-19 Size : 708kb Publisher : alamin
[Other] BrainStudySpeechRecognitionusingNeuralNetworks.ra DL : 0: (Brain Study)Speech Recognition using Neural Networks
Update : 2025-02-19 Size : 708kb Publisher : honey
[Speech/Voice recognition/combine] Speech-Recognition-Using-Neural-Networks DL : 0: speech recognision is used to develop automatic speech regonision system and also to normalise it and then map it to a particular user voice.
Update : 2025-02-19 Size : 40kb Publisher : anusha123
[AI-NN-PR] AUDIOANN DL : 0: 运用神经网络识别语音，采用了MFCC的系数，来识别语音的研究生论文-Speech recognition using neural networks, using MFCC coefficients to identify the voice of the graduate thesis
Update : 2025-02-19 Size : 113kb Publisher : awdawd
[Program doc] Speech-Recognition-using-NN-Using-WEKA-Tool DL : 0: This good paper on speech recognition using neural networks with WEKA tool-This is good paper on speech recognition using neural networks with WEKA tool
Update : 2025-02-19 Size : 54kb Publisher : siva
[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.
Update : 2023-07-03 Size : 11.11mb Publisher : ihaveap1