Paper: Learning To Say It Well: Reranking Realizations By Predicted Synthesis Quality

ACL ID P06-1140
Title Learning To Say It Well: Reranking Realizations By Predicted Synthesis Quality
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

This paper presents a method for adapting a language generator to the strengths and weaknesses of a synthetic voice, thereby improving the naturalness of synthetic speech in a spoken language dialogue sys- tem. The method trains a discriminative reranker to select paraphrases that are pre- dicted to sound natural when synthesized. The ranker is trained on realizer and syn- thesizer features in supervised fashion, us- ing human judgements of synthetic voice quality on a sample of the paraphrases rep- resentative of the generator’s capability. Results from a cross-validation study indi- cate that discriminative paraphrase rerank- ing can achieve substantial improvements in naturalness on average, ameliorating the problem of highly variable synthesis qual- ity typically encountered with tod...