Speech Recognition Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Speech Recognition ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Speech Recognition Projects
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Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG
This project is about teaching computers to understand human emotions. It uses face expressions, voice, and brain signals to detect how a person feels. The system combines these three types of data to make emotion recognition more accurate and faster. Experiments show that this method works better than older approaches. -
Improving Dysarthric Speech Segmentation With Emulated and Synthetic Augmentation
This project focuses on improving speech analysis for people with neurological diseases. It uses computer models to break speech into individual words automatically. The study shows that adding simulated or altered speech from healthy people helps the model work better on impaired speech. This method reduces the need for real clinical recordings while keeping high accuracy. -
The Extent of AI Applications in EFL Learning and Teaching
This project studies how artificial intelligence (AI) can be used to teach English as a foreign language. It reviews existing research on AI tools and methods in language learning. The study looks at how AI affects students’ language skills, how students and teachers feel about using AI, and the challenges of using these technologies. It also points out areas where more research is needed. -
A Novel Convolutional Neural Network Model for Automatic Speaker Identification From Speech Signals
This project focuses on identifying people by their voice using a computer model. It uses a deep learning method called CNN to recognize who is speaking from their recorded speech. The system was trained with English audio from several people and tested with other datasets. It achieved very high accuracy, showing that it can correctly identify speakers in most cases.
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