Vgram
Feature extraction from sequential data
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Nlpython | 302 | 2 years ago | 26 | mit | Jupyter Notebook | |||||
| This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing" | ||||||||||
| Speech_signal_processing_and_classification | 203 | a year ago | 3 | mit | Python | |||||
| Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8]. | ||||||||||
| Bert Attributeextraction | 185 | 5 years ago | 2 | Python | ||||||
| USING BERT FOR Attribute Extraction in KnowledgeGraph. fine-tuning and feature extraction. 使用基于bert的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取。 | ||||||||||
| Lingfeat | 113 | a year ago | 6 | cc-by-sa-4.0 | Python | |||||
| LingFeat - A Comprehensive Linguistic Features Extraction ToolKit for Readability Assessment | ||||||||||
| Bertqa Attention On Steroids | 105 | 5 years ago | 1 | apache-2.0 | Jupyter Notebook | |||||
| BertQA - Attention on Steroids | ||||||||||
| Lftk | 84 | 3 months ago | 18 | April 27, 2023 | other | Python | ||||
| [BEA @ ACL 2023] General-purpose tool for linguistic features extraction; Tested on readability assessment, essay scoring, fake news detection, hate speech detection, etc. | ||||||||||
| Documentfeatureselection | 37 | 6 years ago | 19 | October 24, 2018 | 4 | other | Python | |||
| A set of metrics for feature selection from text data | ||||||||||
| Arabic Tagger | 31 | 11 years ago | 1 | gpl-3.0 | Java | |||||
| AQMAR Arabic Tagger: Sequence tagger with cost-augmented structured perceptron training | ||||||||||
| Tscan | 18 | 7 months ago | 32 | agpl-3.0 | C++ | |||||
| T-scan: an analysis tool for dutch texts to assess the complexity of the text, based on original work by Rogier Kraf | ||||||||||
| Blabla | 18 | 3 years ago | 5 | July 29, 2020 | 9 | gpl-3.0 | Python | |||
| Novoic's linguistic feature extraction library | ||||||||||
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