Experiments on speaker profile portability

This paper addresses the problem of speaker characterization in the speaker-dependent speech recognition problem. Speaker Adaptation and Normalization techniques are designed to reduce the mismatch introduced by inter-speaker variability. Yet there is another source of mismatch introduced by intra-speaker variability. Indeed, the speaking style of a speaker depends on the nature of the speech uttered. The framework of this paper is speaker-dependent isolated-word recognition on an embedded engine. The limited computational and memory loads of this engine reduce the possible techniques for normalization. The proposed solution uses a speaker profile trained on dictation data and exported to the embedded engine. In this framework we study the portability of a task-dependent speaker profile from dictation task to command task. Experiments have been conducted on a Scansoft 255 speakers database. We show that the portability results in a loss of efficiency due to the nature of the considered tasks.