HMM-based Speech Recognition in Noisy Environments Using hmm Files

The hmm files implement Hidden Markov Model (HMM) algorithm for speech recognition under noisy conditions. Key components include: vad.m for endpoint detection using energy-based thresholding; mfcc.m for Mel-Frequency Cepstral Coefficients extraction with filter bank processing; pdf.m computing Gaussian probability density output for observation vectors; mixture.m calculating state output probabilities through Gaussian mixture modeling; getparam.m deriving forward/backward probabilities and scaling coefficients; viterbi.m implementing Viterbi algorithm for optimal path decoding; baum.m executing Baum-Welch algorithm for parameter re-estimation; inithmm.m initializing HMM parameters; train.m handling model training procedures.

MFCC Feature Extraction Code Implementation

MFCC parameter extraction is crucial in speech recognition systems, offering both efficient storage and compatibility with human auditory perception. This MATLAB simulation code provides complete and high-performance feature extraction functionality, implementing the standard MFCC algorithm with optimized signal processing techniques.

Algorithm for Extracting MFCC Parameters from Audio Signals

Self-developed algorithm for extracting MFCC (Mel-frequency cepstral coefficients) parameters from audio signals, implemented in MATLAB with comprehensive code documentation. Welcome researchers and developers to reference and utilize this implementation.

Calculating MFCC Parameters (Mel-Frequency Cepstral Coefficients)

MATLAB implementation for computing MFCC parameters (Mel-Frequency Cepstral Coefficients), LPCC parameters (Linear Predictive Cepstral Coefficients), and pitch (fundamental frequency) with signal processing techniques