MFCC Feature Extraction for Speech Emotion Recognition
MFCC Feature Extraction for Speech Parameters with Emotion Differentiation Capabilities and Algorithm Implementation Insights
Mel-Frequency Cepstral Coefficients (MFCC) Algorithm for Speech Feature Extraction
A proven MFCC speech feature extraction algorithm with successful debugging and validation, featuring implementation insights and key signal processing steps.
HMM Code for MATLAB Speech Recognition
dtw - DTW algorithm demonstration program mfcc.m - MFCC parameter calculation program dtw.m - Basic DTW algorithm implementation dtw2.m - Optimized DTW algorithm testdtw.m - DTW algorithm testing program vad.m - Endpoint detection program cdhmm - Continuous Gaussian Mixture HMM demonstration pdf.m - Gaussian probability density function mixture.m - Gaussian mixture output probability inithmm.m - HMM parameter initialization getparam.m - Observation sequence parameter calculation viterbi.m - Viterbi algorithm for speech recognition
Implementation of MFCC-Based Speech Recognition System Simulation
Complete simulation of a speech recognition system based on MFCC with source files for training implementation included in the package
MFCC - Mel-Frequency Cepstral Coefficients
MFCC, or Mel-Frequency Cepstral Coefficients, represent one of the fundamental features in speech signal processing that effectively models human auditory perception. The computational pipeline involves preprocessing, windowing, Fourier transformation, power spectrum calculation, natural logarithm application, and discrete cosine transform (DCT). The MATLAB implementation leverages a speech processing toolbox available for online download, with key functions including frame segmentation, FFT operations, and Mel-filterbank integration.
Speech Recognition Using DTW and MFCC Algorithms
Implementation of speech recognition system combining Dynamic Time Warping and Mel-Frequency Cepstral Coefficients
Continuous Digit Speech Recognition System
A program for continuous digit speech recognition that extracts MFCC features and implements recognition using Dynamic Time Warping (DTW) algorithm, complete with comprehensive documentation
MATLAB Implementation of Speaker Recognition with Neural Network Model
Speaker recognition code implementation featuring endpoint detection, pre-emphasis, MFCC feature extraction, and neural network classification model
Parameter Extraction for LPCC and MFCC in Speaker Recognition
Implementation of LPCC and MFCC parameter extraction algorithms for speaker recognition systems with code examples
Two Feature Extraction Methods in Speech Recognition: LPCC and MFCC
Overview of LPCC and MFCC feature extraction methods in speech recognition, along with text-independent DTW recognition algorithm and preprocessing noise cancellation techniques. These are thoroughly tested implementations with practical code integration insights.