An Approach to Low Footprint Pronunciation Models for Embedded Speaker Independent Name Recognition

Pronunciation modeling is an important component of speaker independent name recognition on embedded devices. Decision trees have been widely used to generate pronunciations of names due to improved accuracy. However, pronunciation modeling using decision trees may suffer from two main draw backs. The first is large memory footprint. The second is that decision trees usually generate a single pronunciation which does not reflect the real-world multiple pronunciations of a name. We present an approach to address these draw backs. The approach consists of a letter-to-phoneme mapping method that prunes many irregular pronunciations in order to train compact decision trees, and a multi-stage pronunciation transformation method that generates multiple pronunciations from the output of the trained decision trees. The approach effectively reduces footprint by more than 58% and achieves more than 23% of word error rate reduction, compared to a baseline.