A turbo-style algorithm for lexical baseforms estimation

In this research, an iterative and unsupervised Turbo-style algorithm is presented and implemented for the task of automatic lexical acquisition. The algorithm makes use of spoken examples of both spellings and words and fuses information from letter and subword recognizers to boost the overall lexical learning performance. The algorithm is tested on a challenging lexicon of restaurant and street names and evaluated in terms of spelling accuracy and letter error rate. Absolute improvements of 7.2% and 3% (15.5% relative improvement) are obtained in the spelling accuracy and the letter error rate respectively following only 2 iterations of the algorithm.