Learning to Say It Well: Reranking Realizations by Predicted Synthesis Quality

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 system. The method trains a discriminative reranker to select paraphrases that are predicted to sound natural when synthesized. The ranker is trained on realizer and synthesizer features in supervised fashion, using human judgements of synthetic voice quality on a sample of the paraphrases representative of the generator's capability. Results from a cross-validation study indicate that discriminative paraphrase reranking can achieve substantial improvements in naturalness on average, ameliorating the problem of highly variable synthesis quality typically encountered with today's unit selection synthesizers.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Crystal Nakatsu, Michael White
• Keywords: ACL 2006, ACL 2007, Paraphrase, Synthetic Voice, Synthetic Voice Quality