Eigenvoice Speaker Adaptation with Minimal Data for Statistical Speech Synthesis Systems Using a MAP Approach and Nearest-Neighbors

Statistical speech synthesis (SSS) systems have the ability to adapt to a target speaker with a couple of minutes of adaptation data. Developing adaptation algorithms to further reduce the number of adaptation utterances to a few seconds of data can have substantial effect on the deployment of the technology in real-life applications such as consumer electronics devices. The traditional way to achieve such rapid adaptation is the eigenvoice technique which works well in speech recognition but known to generate perceptual artifacts in statistical speech synthesis. Here, we propose three methods to alleviate the quality problems of the baseline eigenvoice adaptation algorithm while allowing speaker adaptation with minimal data. Our first method is based on using a Bayesian eigenvoice approach for constraining the adaptation algorithm to move in realistic directions in the speaker space to reduce artifacts. Our second method is based on finding pre-trained reference speakers that are close to the target speaker and utilizing only those reference speaker models in a second eigenvoice adaptation iteration. Both techniques performed significantly better than the baseline eigenvoice method in the objective tests. Similarly, they both improved the speech quality in subjective tests compared to the baseline eigenvoice method. In the third method, tandem use of the proposed eigenvoice method with a state-of-the-art linear regression based adaptation technique is found to improve adaptation of excitation features.