Blind equalization via minimization of VQ distortion for ETSI standard DSR front-end

We present blind equalization techniques for the ETSI standard distributed speech recognition (DSR) front-end which compensate for acoustic mismatch caused by input devices. The DSR front-end employs vector quantization (VQ) for feature parameter compression so that the mismatch does not only cause a shift of parameters but also increases VQ distortion. Although CMS is one of the most effective methods to compensate for the shift, it cannot decrease VQ distortion in DSR. To compensate for the shift and decrease VQ distortion simultaneously, the proposed methods estimate the shift in the input data necessary to match the VQ codebook distribution. The methods do not need the acoustic likelihood, which is calculated in a decoder on the server side. Therefore, they are applicable to the DSR front-end. The Japanese Newspaper Article Sentences database (JNAS) was used for the equalization experiments. While the word error rate (WER) for the ETSI standard DSR front-end was 18.6 % under an acoustic mismatched condition, our proposed method yielded a rate of 12.3 %.