Speech Recognition Implementation Using MATLAB Code
MATLAB-based speech recognition program featuring basic functionality implementation with potential for advanced feature integration
VQ-Based Speaker Recognition System Implementation
Course Design Requirements and Objectives Speech recognition represents a critical component in human-computer interface design and serves as a vital application technology in speech signal processing. Vector Quantization (VQ) has demonstrated excellent performance in isolated word recognition systems, particularly through Finite-State Vector Quantization (FSVQ) techniques that significantly enhance recognition accuracy. VQ-based isolated word recognition systems offer superior comprehensive characteristics including high classification accuracy, minimal storage requirements, and real-time response capabilities. This course project requires designing a speaker-dependent isolated word recognition system using VQ methodology. Utilizing MATLAB tools, students will develop VQ codebook training programs and recognition algorithms to identify specific voice commands such as "Up," "Down," "Left," and "Right." Key implementation components include: 1. Designing a speaker-dependent isolated word recognition system based on VQ technology, encompassing voice acquisition and recognition detection experiments 2. Developing MATLAB simulation programs with system modulation and performance analysis 3. Implementing feature extraction algorithms (e.g., MFCC) and VQ codebook generation using LBG algorithm 4. Creating pattern matching mechanisms through distance calculation functions like Euclidean distance measurement
MATLAB Program for Speech Recognition with Audio Files and Algorithms
A comprehensive MATLAB program for speech recognition, including sample audio files and implementing various recognition algorithms such as feature extraction and pattern matching techniques.
Using Gradient Descent Method for Unconstrained Optimization Solutions
Speech signal processing and speech recognition generally involve preprocessing, feature extraction, vectorization, and matching calculation stages. This guide explains how to implement filtering, framing, windowing, and endpoint detection on speech signals using MATLAB, with specific code examples and algorithm descriptions. Note: The Voicebox toolbox (available online) provides essential functions for comprehensive speech processing implementations.
Computation of Hilbert-Huang Spectrum
Implementation program for Hilbert-Huang Spectrum calculation, where extracted spectral information can be applied to speech recognition, fault detection, and other signal processing applications. The algorithm involves Empirical Mode Decomposition (EMD) and Hilbert spectral analysis for time-frequency feature extraction.
DAP Source Code for Speech Recognition Implementation
Highly practical DAP source code for speech recognition applications - featuring robust feature extraction and acoustic modeling capabilities. Ideal for developers seeking to implement efficient voice processing systems.
Speech Recognition Using Gabor Transform with Implementation Insights
Implementation of Gabor transform for speech recognition, originally sourced from Cambridge Laboratory's website. Includes enhanced technical details about filter bank configuration, feature extraction methods, and recommended MATLAB/Python code approaches for time-frequency analysis.
MATLAB Code Implementation for Speech Recognition
This MATLAB-based speech recognition program implements voice detection and filtering techniques, featuring comprehensive audio signal processing capabilities for accurate speech analysis.
Maximum Likelihood Linear Regression (MLLR) Algorithm for Speech Recognition
Maximum Likelihood Linear Regression (MLLR) Algorithm for Speech Recognition with Implementation Insights
Energy Spectrum Analysis for Voice Information in Speech Recognition
Performing energy spectrum analysis on voice information in speech recognition to obtain spectrograms and statistically analyze frequency characteristics across all audio data.