Syllable based Turkish speech recognition using Dynamic Time Warping and Multilayer Perceptron

We have implemented syllable based isolated word Turkish speech recognition systems with Dynamic Time Warping (DTW) and Multilayer Perceptron (MLP) in this study. Lpc, parcor, cepstrum and mfcc features are used for these applications on the dictionary which includes 200 words. After recording the word utterances, the onsets of syllables are determined and the syllable feature database is constructed. Using this database, the most similar syllables are decided by DTW and MLP. The recognized syllables are concatenated in order. If the constructed word is in the dictionary, the recognized word is found. According to the features, the best results are obtained by DTW with mfcc features. The recognition accuracy rates are 95.1% and 92.6% by DTW and MLP recpectively.