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.

A stand-alone OpenSmile plugin for the Cordova framework.

Codes for extracting features of IEMOCAP with Open Smile tool kit

Audio classification research in PyTorch: Transformer models, OpenSMILE/MEL features, VIB and encoder-only DP experiments. Synthetic CPU checks and speaker-aware cross-validation. 隐私感知音频学习

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

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

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.

This is a project dedicated to the classification of emotional speech and was created in class with Prof. Dr. Burkhardt at Technische Universität Berlin.