Year: 2014
Type: conference-paper
Abstract: Incorrect normalization of text can be particularly damaging for applications like text-to-speech synthesis (TTS) or typing auto-correction, where the resulting normalization is directly presented to the user, versus feeding downstream applications.In this paper, we focus on abbreviation expansion for TTS, which requires a "do no harm", high precision approach yielding few expansion errors at the cost of leaving relatively many abbreviations unexpanded.In the context of a largescale, real-world TTS scenario, we present methods for training classifiers to establish whether a particular expansion is apt.We achieve a large increase in correct abbreviation expansion when combined with the baseline text normalization component of the TTS system, together with a substantial reduction in incorrect expansions.
DOI: 10.3115/v1/p14-2060
Authors: Brian Roark, Richard Sproat
Institution: Google (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2252192936