opensmile

Speech emotion recognition implemented in Keras (LSTM, CNN, SVM, MLP) | 语音情感识别

Human Emotion Understanding using multimodal dataset.

ADHD_Recognition with personal voice

Predictive modeling of users' interpersonal characteristics by the sound of their voices and manner of speaking.

CNN for language-independent emotion prediction on a dataset of English and French speech samples.

Visual Studio Code extension that adds syntax highlighting, diagnostics and IntelliSense features for openSMILE configuration files

speech emotion recognition, augmentation

Script to extract acoustic features from speech using OpenSmile toolkit.

acoustic low-level descriptors (LLD) feature extraction via the opensmile software

Hyb-DysNet: hybrid feature fusion (eGeMAPSv02 + Wav2Vec2-XLS-R + Librosa) and soft-voting ensemble for dysarthria severity classification in ALS patients.

Codes for extracting features of IEMOCAP with Open Smile tool kit

A comprehensive framework for detecting hate speech in the low-resource Telugu language using multimodal (audio and text) analysis

A stand-alone OpenSmile plugin for the Cordova framework.

Research project analyzing the impact of audio features on Spotify podcast performance.

Local-first multimodal sentiment prototype combining facial emotion, offline ASR, text sentiment, and voice features.

Analysis Scripts for Journal Paper Mannuscript

Code for "Do CEO Voices Move Markets?" — vocal stress and analyst tone in earnings calls as predictors of high-frequency order flow. Princeton ORF Senior Thesis (2026).

This repository contains a complete machine learning pipeline for Speech Emotion Recognition (SER) using Deep Neural Networks (DNNs).

An AI-powered text and voice intelligence web app that detects and clasifies tone, rewrites transcribed text into a target tone, enhances emotional mood, and checks grammar — built with React + Vite, Gradio/Hugging Face models, Firebase auth, and an immersive Three.js UI.