hnbrh

A-Benchmark-Dataset-for-Learning-to-Intervene-in-Online-Hate-Speech

asr_preprocessing

Python implementation of pre-processing for End-to-End speech recognition

AudioNet-V1

1D CNN based classifier for Speech Commands Dataset

AVSR-datasets

Audio-visual Speech Recognition Datasets

awesome-speech-enhancement

A curated list of awesome Speech Enhancement papers, libraries, datasets, and other resources.

CoreNLP

Stanford CoreNLP: A Java suite of core NLP tools.

Create_Speech_Dataset

Creates a speech dataset for deep learning

GenderClassifierLibriSpeech

Gender Classification of the speaker from LibriSpeech Dataset

Speech-Command-Recognition-with-Capsule-Network

Speech command recognition with capsule network & various NNs / KWS on Google Speech Command Dataset.

VERBO-emotional-speech-dataset

VERBO (Voice Emotion Recognition dataBase in pOrtuguese language)

hnbrh/GenderClassifierLibriSpeech

Gender Classification of the speaker from LibriSpeech Dataset

hnbrh/A-Benchmark-Dataset-for-Learning-to-Intervene-in-Online-Hate-Speech

hnbrh/awesome-speech-enhancement

A curated list of awesome Speech Enhancement papers, libraries, datasets, and other resources.

hnbrh/CoreNLP

Stanford CoreNLP: A Java suite of core NLP tools.

hnbrh/css10

CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages

hnbrh/datasets-CMU_Wilderness

CMU Wilderness Multilingual Speech Dataset

hnbrh/Datasets-for-Hate-Speech-Detection

Datasets for Hate Speech Detection

hnbrh/datasets_emotion

This repository collects information about different data sets for Music Emotion Recognition and Speech Emotion Recognition.

hnbrh/emotion-recognition-using-speech

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

hnbrh/hate-speech-and-offensive-language

Repository for the paper "Automated Hate Speech Detection and the Problem of Offensive Language", ICWSM 2017

hnbrh/id-hatespeech-detection

The Dataset for Hate Speech Detection in the Indonesian Language (Bahasa Indonesia)

hnbrh/id-multi-label-hate-speech-and-abusive-language-detection

The Dataset for Multi Label Hate Speech and Abusive Language Detection in Indonesian Twitter

hnbrh/jejueo

Jejueo Datasets for Machine Translation and Speech Synthesis

hnbrh/Living-Audio-Dataset

A "Crowd-Built" continuously growing speech dataset with transcripts. The dataset contains multiple languages and is intended for anyone to be able to add to it.

hnbrh/MNIST-MIX

This is the repository for our study "MNIST-MIX: A Multi-language Handwritten Digits Recognition Dataset".

hnbrh/multimodal-speech-emotion-recognition

Lightweight and Interpretable ML Model for Speech Emotion Recognition and Ambiguity Resolution (trained on IEMOCAP dataset)

hnbrh/nlp-datasets

Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing (NLP)

hnbrh/nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

hnbrh/pySpeechRev

This python code performs an efficient speech reverberation starting from a dataset of close-talking speech signals and a collection of acoustic impulse responses.

hnbrh/Speech-Commands-Classification-by-LSTM-PyTorch

Classification of 11 types of audio clips using MFCCs features and LSTM. Pretrained on Speech Command Dataset with intensive data augmentation.

hnbrh/speech-nlp-datasets

Contains links to publicly available datasets for modeling health outcomes using speech and language.

hnbrh/Speech-Separation

Here we will develop a general model for Speech Separation.Without any dependency on wsj0 dataset.

hnbrh/Speech_Recognition_Kaggle_Dataset

Recognizes 10 simple english phrases. Convolutional Neural Network, Spectrogram

hnbrh/Speech_Signal_Processing_and_Classification

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].

hnbrh/spoken_language_dataset

The dataset with English, German and Spanish speech samples.

hnbrh/swedish-asr-dataset

Jupyter Notebooks for creating Speech datasets

hnbrh/TPGST-Tacotron

Google's TPGST reimplementation.

hnbrh/TTS-Portuguese-Corpus

Open Source Text To Speech Portuguese Dataset

hnbrh/UQSpeechDataset

Uyghur Single Speaker Speech Dataset. ウイグル語音声データセット

hnbrh/VAD

Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.