A Syllable Based Approach for Improved Recognition of Spoken Names

Recognition of spoken names is a challenging task for speech recognition systems because of the large variations in speaking styles, linguistic origins and pronunciation found in names. The complex linguistic nature of names makes it difficult to automatically generate pronunciation variations. For many applications the list of names tends to be in the order of several hundred thousands, making spoken name recognition a high perplexity task. Use of multiple pronunciations to account for the variations in names further increases the perplexity of the recognition system substantially. In this paper we propose the use of the syllable as the acoustic unit for spoken name recognition and show how pronunciation variation modeling with syllables can help in improving recognition performance and reducing the system perplexity. We present results comparing systems which use context dependent phones with syllable based systems, and demonstrate that a significant increase in recognition accuracy and speed, can be achieved by using the syllable as the acoustic unit for spoken name recognition. With a finite state grammar network for spoken name recognition, the observed recognition error rate for the syllable-based system was 40% less than the phone-based system. For syllable bigram based information retrieval schemes the observed recognition error rate was about 60% less than the corresponding phone system.