An improved VQ based algorithm for recognizing speaker-independent isolated words

In this paper, an improved codebook generation algorithm called SLVQ (Speaker Level Vector quantization) is proposed, which can improve the recognition accuracy of speaker independent isolated words. Linde-Buzo-Gary (LBG) algorithm is the most commonly used codebook design method. The idea behind LBG is to find an optimal codebook that minimizes the distortion between the training words and the codebook. But this does not guarantee that the testing words also have minimum distortion as training words. To address the problem of producing poor codebook for testing words in speaker independent speech recognition, the proposed method makes use of the diversity of different speakers by randomly selecting some speakers and their pronounced words in the codebook design procedure to optimize codebooks. An evaluation experiment has been conducted to compare the speech recognition performance of the codebooks produced by the LBG, the LVQ (learning vector quantization), and the SLVQ. It is clearly shown that the SLVQ method performs better than the other two methods.