Speech Recognition Using DTW and MFCC Algorithms

The speech recognition technology based on Dynamic Time Warping (DTW) and Mel-Frequency Cepstral Coefficients (MFCC) represents a widely adopted approach in the field of speech recognition. Dynamic Time Warping serves as an algorithm for measuring similarity between two temporal sequences that may vary in speed or duration, while MFCC provides a mathematical representation method for extracting acoustic features from speech signals. In implementation, MFCC feature extraction typically involves multiple processing stages: pre-emphasis to enhance high frequencies, framing the signal into short segments, applying window functions (like Hamming windows), computing power spectrum through FFT, mapping to Mel-scale using filter banks, and finally performing discrete cosine transform to obtain cepstral coefficients. These coefficients effectively capture the characteristics of human auditory perception. The DTW algorithm then dynamically warps the time axis to align test feature vectors with reference templates, calculating the optimal path with minimum cumulative distance. Practical implementations often use dynamic programming with recurrence relations to compute the warping path efficiently. Common approaches include implementing constraints like Sakoe-Chiba band to reduce computational complexity. By integrating these two methodologies, we can achieve more accurate identification and interpretation of speech signals, thereby attaining higher-performance speech recognition capabilities. The combination allows for effective handling of temporal variations in speech patterns while maintaining robust feature representation.