Pronunciation guided by automatic speech recognition

Abstract

Speech synthesis chooses pronunciations of words with multiple acceptable pronunciations based on an indication of a personal, class-based, or global preference or an intended non-preferred pronunciation. A speaker's words can be parroted back on personal devices using preferred pronunciations for accent training. Degrees of pronunciation error are computed and indicated to the user in a visual transcription or audibly as word emphasis in parroted speech. Systems can use sets of phonemes extended beyond those generally recognized for a language. Speakers are classified in order to choose specific phonetic dictionaries or adapt global ones. User profiles maintain lists of which pronunciations are preferred among ones acceptable for words with multiple recognized pronunciations. Systems use multiple correlations of word preferences across users to predict use preferences of unlisted words. Speaker-preferred pronunciations are used to weight the scores of transcription hypotheses based on phoneme sequence hypotheses in speech engines.