The neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)

Human emotions are one of the strongest ways of communication. Even if a person doesn’t understand a language, he or she can very well understand the emotions delivered by an individual. In other words, emotions are universal.The idea behind the project is to develop a Speech Emotion Analyzer using deep-learning to correctly classify a human’s different emotions, such as, neutral speech, angry speech, surprised speech, etc. We have deployed three different network architectures namely 1-D CNN, LSTMs and Transformers to carryout the classification task. Also, we have used two different feature extraction methodologies (MFCC & Mel Spectrograms) to capture the features in a given voice signal and compared the two in their ability to produce high quality results, especially in deep-learning models.

Speech Emotion Analyzer application for Real-Time Speech Emotion Recognition using Audio Segmentation and State Machines thesis.

Mango is a mobile app demo about the Watson Speech-to-Text, it can provide the real-time transcription and translation, it can record the audio, translate the language you preferred, and store the audio on the mobile device. Later we'll add Watson Tone Analyzer to analyze the audio you record, it uses linguistic analysis to detect and interpret emotions, social tendencies, and language style cues found in your audio.

machine-learning

Has there ever been a situation where you were unsure how a sentence or paragraph is perceived by others? With the help of this app, Speech Audit, and IBM's Speech to Text and Tone Analyzer API, users are able to see what emotion others may perceive in your sentence! This is the API of the Application

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***CUNY Hackathon Winner*** A healthcare based app that built on IBM Watson’s techniques to prevent depression. The pet can communicate with users and give response based on users’ emotions. The conversation will be recognized by IBM Watson's Speech-to-Text API and be analyzed using IBM Watson's Tone Analyzer.

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Speech emotion Analyzer using CNN

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Deep learning project to analyze emotions of a speaker from an audio clip.

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Created an emotion detection system with 2,000 audio files, achieving 70% validation accuracy using a CNN. Improved accuracy by 15% through gender-based separation and optimized features with Librosa, enabling effective emotion classification for AI applications.

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Human emotion identification from speech

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Speech Emotion Analyzer (SEA) - project to determinate human speech emotions from audio using deep learning

It was the machine learning project of Speech Emotion Analyzer to find the emotion by extracting the features of the voices.

AI-powered Smart Interview Analyzer that evaluates candidate responses using NLP, Speech Recognition, emotion detection, and a weighted scoring system with real-time feedback. Integrated with TiDB Cloud for data storage.

Cognisense – A Multi-Modal Sentiment Analyzer is an intelligent AI-based system designed to analyze and interpret human emotions by combining multiple data modalities such as text, audio, and visual inputs. It integrates natural language (NLP), speech pattern recognition and facial expression to provide a more accurate understanding of sentiment.

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Emotion Speech Analyzer

Speech Emotion Analyzer

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The idea behind creating this project was to build a machine learning model that could detect emotions from the speech we have with each other all the time. Nowadays personalization is something that is needed in all the things we experience everyday.