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Testing the Consistency Assumption: Pronunciation Variant Forced Alignment in Read and Spontaneous Speech Synthesis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5155-5159
Number of pages5
ISBN (Print)978-1-4799-9988-0
DOIs
Publication statusPublished - Mar 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - China, Shanghai, China
Duration: 20 Mar 201625 Mar 2016
https://www2.securecms.com/ICASSP2016/Default.asp

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Abbreviated titleICASSP 2016
CountryChina
CityShanghai
Period20/03/1625/03/16
Internet address

Abstract

Forced alignment for speech synthesis traditionally aligns a phoneme sequence predetermined by the front-end text processing system. This sequence is not altered during alignment, i.e., it is forced, despite possibly being faulty. The consistency assumption is the assumption that these mistakes do not degrade models, as long as the mistakes are consistent across training and synthesis. We present evidence that in the alignment of both standard read prompts and spontaneous speech this phoneme sequence is often wrong, and that this is likely to have a negative impact on acoustic models. A lattice-based forced alignment system allowing for pronunciation variation is implemented, resulting in improved phoneme identity accuracy for both types of speech. A perceptual evaluation of HMM-based voices showed that spontaneous models trained on this improved alignment also improved standard synthesis, despite breaking the consistency assumption

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