Explore MATLAB source code curated for "语音识别" with clean implementations, documentation, and examples.
UIUC's DTW+VQ-based speech recognition system with samples - simply rename .txt files to .m for immediate use, featuring dynamic time warping and vector quantization algorithms for pattern matching and feature compression.
A MATLAB-based Hidden Markov Model implementation method for isolated word speech recognition, featuring complete training and recognition algorithms with practical code implementation details.
Design a digital audio effects processor capable of performing various audio effect processing on speech signals. Requirements: (1) Input speech signal source must be collected from real-world environments; (2) Implement at least 3 audio processing functions: 1. Voice recognition for 5 project team members; 2. Noise reduction for at least 3 types of noise in real speech samples exceeding 10 seconds; 3. Equalizer functionality; (3) Control via human-machine interface with audio output through speakers/headphones.
Complete MATLAB program for speech recognition workflow with functional testing capabilities. This implementation includes signal preprocessing, feature extraction (MFCC), and classification algorithms. I'm sharing my foundational code library to help fellow researchers when other resources are scarce.
A comprehensive speech recognition MATLAB program developed after extensive research, featuring practical implementations of audio processing algorithms and pattern recognition techniques for educational and application purposes.
Source code for dynamic time warping (DTW) implementation demonstrating feature vector extraction and comparison in speech recognition systems
A comprehensive MATLAB-based speech recognition system designed to convert spoken input into text output using advanced signal processing algorithms and feature extraction techniques.
Neural network speech recognition technology and its implementation using deep learning architectures for converting human speech into text.
Independent Component Analysis (ICA) has evolved over the past two decades as a blind source separation method. This statistical technique aims to recover statistically independent source signals from mixed signals collected by sensors, with applications spanning speech recognition, telecommunications, and biomedical signal processing. This article systematically examines ICA's development, fundamental principles, implementation approaches, and major algorithms including FastICA, with enhanced technical descriptions of computational methods and practical applications.
This MATLAB toolbox for speech recognition and speech enhancement serves as an excellent reference implementation, featuring key algorithms like spectral subtraction, Wiener filtering, and deep learning-based enhancement techniques with practical code examples.