iVector-based discriminative adaptation for automatic speech recognition

Abstract

We presented a novel technique for discriminative feature-level adaptation of automatic speech recognition system. The concept of iVectors popular in Speaker Recognition is used to extract information about speaker or acoustic environment from speech segment. iVector is a low-dimensional fixed-length representing such information. To utilized iVectors for adaptation, Region Dependent Linear Transforms (RDLT) are discriminatively trained using MPE criterion on large amount of annotated data to extract the relevant information from iVectors and to compensate speech feature. The approach was tested on standard CTS data. We found it to be complementary to common adaptation techniques. On a well tuned RDLT system with standard CMLLR adaptation we reached 0.8% additive absolute WER improvement.