Speaker independent acoustic modeling using speaker normalization

This paper proposes a novel speaker-independent (SI) modeling for spontaneous speech data from multiple speakers. The SI acoustic model parameters are estimated by individual training for inter-speaker variability and for intraspeaker phonetically related variation in order to obtain a more accurate acoustic model. The linear transformation technique is used for the speaker normalization to extract intra-speaker phonetically related variation and also is used for the re-estimation of inter-speaker variability. The proposed modeling is evaluated for a Japanese spontaneous speech data, using continuous density mixture Gaussian HMMs. Experimental results from the use of proposed acoustic model show that the reductions in word error rate can be achieved over the standard SI model regardless the type of acoustic model used.