Recognition of spelled names over the telephone

Recognition of spelled names over the telephone line is essential for applications such as telephone directory assistance, or automatic mail ordering. We present recognition results on the spelling section of the OGI Spelled and Spoken Word Telephone Corpus, using a Multi-State Time Delay Neural Network (MS-TDNN). Many applications allow for strong language modeling constraints. In our experiments we examined the bene cial e ects of reducing the search space to a list of last names, ranging from about 1000 to 14 million entries. We compare tree search methods and show that signi cant improvements can be achieved by enriching the search trees with probabilities.