Search - algorithm of speech recognition

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[Other] speech DL : 0: 语音识别特征提取算法的研究及实现的相关文献-Speech recognition feature extraction algorithm and implementation of the relevant literature
Date : 2026-01-01 Size : 2.66mb User : 李洋
[Other] Survey-on-speech-emotion-recognition DL : 0: 介绍了语音情感识别系统的组成, 重点对情感特征和识别算法的研究现状进行了综述, 分析了主要的语音情感特征, 阐述了代表性的语音情感识别算法以及混合模型, 并对其进行了分析比较。最后, 指出了语音情感识别技术的可能发展趋势-Speech emotion recognition system composed Research focus on emotional characteristics and recognition algorithms are reviewed, the analysis of the main voice emotional characteristics of a representative of the speech emotion recognition algorithm as well as hybrid model and its analysis comparison. Finally, it is pointed out that the speech emotion recognition technology possible trends
Date : 2026-01-01 Size : 182kb User : wll
[Other] yuxinshibie DL : 0: 基于MATLAB的计算机语音识别的研究，对DTW算法和HMM算法的研究-MATLAB computer speech recognition-based research, the study of the DTW algorithms and HMM algorithm
Date : 2026-01-01 Size : 1.39mb User : 张亚曼
[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