s-fatemeh-ebrahimi Goto Github PK
Name: S. F. (Mona)_Ebrahimi
Type: User
Company: Sharif University of Technology
Bio: Natural Language Processing- Computational Linguist
Location: Tehran
Name: S. F. (Mona)_Ebrahimi
Type: User
Company: Sharif University of Technology
Bio: Natural Language Processing- Computational Linguist
Location: Tehran
Pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE)
datasets
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (IJCAI 2019)
The repo of the paper "Formality Style Transfer with Shared Latent Space"
Code for "Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization"
Create a standard set of issue labels for a GitHub project
Code for the paper "Language Models are Unsupervised Multitask Learners"
A simple, minimal wrapper for tensorflow's seq2seq module, for experimenting with datasets rapidly
Python port of Moses tokenizer, truecaser and normalizer
A YouTube scraper for scraping channels, playlists, and searching 🔎
A simple speech recognition system using Linear predictive coding (LPC) and tested on spoken digits
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].
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.