MLLR-like speaker adaptation based on linearization of VTLN with MFCC features

In this paper, an MLLR-like adaptation approach is proposed whereby the transformation of the means is performed deter-ministically based on linearization of VTLN. Biases and adap-tation of the variances are estimated statistically by the EM al-gorithm. In the discrete frequency domain, we show that un-der certain approximations, frequency warping with Mel-£lter-bank-based MFCCs equals a linear transformation in the cep-stral domain. Utilizing the deduced linear relationship, the transformation matrix is generated by formant-like peak align-ment. Experimental results using children’s speech show im-provements over traditional MLLR and VTLN. The improve-ments occur even with limited amounts of adaptation data. 1