A STUDY OF ADAPTATION TECHNIQUES ON A VOICEMAIL TRANSCRIPTION TASK

Abstract

Speaker adaptation techniques have emerged as very effective and practical methods to improve ASR performance on a test speaker with only limited speech data from the speaker. We explore the use of adaptation techniques on a new Voicemail database and present some theoretical extensions of the Cluster Transformation (CT) technique. Our experiments on 40 hours of voicemail data and four clusters shows that using cluster information with MLLR improves over baseline MLLR by 2.2% (relative). When the amount of adaptation data in a short message is insufficient to reliably decide its cluster, higher improvements result when we use MLLR for the very short messages and CT on longer ones.