Comments on "Modified K-means algorithm for vector quantizer design".

Previously a modified K-means algorithm for vector quantization design has been proposed where the codevector updating step is as follows: new codevector=current codevector+scale factor (new centroid-current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose the use of a variable scale factor which is a function of the iteration number. For the vector quantization of image data, we show that it offers faster convergence than the modified K-means algorithm with a fixed scale factor, without affecting the optimality of the codebook.

Application of k-Nearest-Neighbor Decision Rule in Vowel Recognition.

The k-nearest-neighbor decision rule is known to provide a useful nonparametric procedure for pattern classification. This rule is applied here to a vowel recognition problem and the effect of the number (k) of nearest neighbors, the size of the trained set and the type of the distance measure on vowel recognition performance is studied. It is shown that the vowel recognition performance remains approximately constant for all the values of k. The recognition performance initially improves with the size of the training set and then converges to an asymptotic value. Selection of a better distance measure leads to a significant improvement in vowel recognition performance.

Synthesis-based recognition of continuous speech.

An acoustic phonemic recognition system for continuous speech is presented. The system utilizes both steady-state and transition segments of the speech signal to achieve recognition. The information contained in formant transitions is utilized by the system by using a synthesis-based recognition approach. It is shown that this improves the performance of the system considerably. Recognition of continuous speech is accomplished here in three stages: segmentation, steady-state recognition, and synthesis-based recognition. The system has been tried out on 40 test utterances, each 3-4 s in duration, spoken by a single male speaker and the following results are obtained: 5.4% missed segment error, 8.3% extra segment error, 52.3% correct recognition using only steady-state segments, and 62.0% correct recognition using both steady-state and transition segments.