Adaptation of a speech recognizer for singing voice

This paper studies the speaker adaptation techniques that can be applied for adapting a speech recognizer to singing voice. Maximum likelihood linear regression (MLLR) techniques are studied, with specific details in choosing the number and types of transforms. The recognition performance of the different methods is measured in terms of phoneme recognition rate and singing-to-lyrics alignment errors of the adapted recognizers. Different methods improve the correct recognition rate with up to 10 percentage units, compared to the non-adapted system. In singing-to-lyrics alignment we obtain a best of 0.94 seconds mean absolute alignment error, compared to 1.26 seconds for the non-adapted system. Global adaptation was found to provide the most improvement in the performance, but small further improvement was obtained with regression tree adaptation.