Isolated Word Speech Recognition System Based on Dynamic Time Warping (DTW)

In the domain of speech processing, several key components require systematic implementation. First, we need to establish a comprehensive speech database containing diverse voice samples to facilitate accurate speech analysis and processing. This typically involves recording multiple speakers under controlled acoustic conditions and storing waveforms in standardized formats like WAV or PCM. Second, audio preprocessing is crucial for noise removal and signal enhancement using techniques like spectral subtraction or Wiener filtering algorithms to improve recognition accuracy. Subsequently, we perform frame segmentation by dividing continuous speech signals into short-term frames (typically 20-30ms) using overlapping window functions such as Hamming windows, which allows for stable feature extraction. Simultaneously, endpoint detection algorithms (like energy-based or zero-crossing rate methods) identify speech boundaries to isolate valid audio segments from silence. Finally, feature analysis extracts discriminative characteristics through algorithms like MFCC (Mel-Frequency Cepstral Coefficients) that capture spectral properties and formant frequencies, enabling effective speech recognition and synthesis tasks. The DTW algorithm then dynamically aligns feature sequences by finding optimal warping paths to handle temporal variations in spoken words.