Unsupervised speaker adaptation methods for vowel templates

Four unsupervised speaker adaptation methods for vowel templates are described and evaluated. There are two approaches to automatically obtaining information on vowel classification and location. One is based on feature parameters and the other on the results of input speech recognition. Here, the former is referred to as a bottom-up approach and the latter as a top-down one. Two adaptation techniques are also presented. The first is template selection from pre-stored sets and the second is template modification. Combining these approaches and techniques, four adaptation methods are derived. These four methods are evaluated in terms of spectral distortion and word recognition rate. They are then compared considering performance, required calculation, rate of correctly used vowels, and type of input speech. The results show that these methods work well and that the top-down approach is better than the bottom-up one. They also show that the modification technique is better than the selection technique.