Projects per year
This paper introduces a method for lightly supervised discriminative training using MMI to improve the alignment of speech and text data for use in training HMM-based TTS systems for low-resource languages. In TTS applications, due to the use of long-span contexts, it is important to select training utterances which have wholly correct transcriptions. In a low-resource setting, when using poorly trained grapheme models, we show that the use of MMI discriminative training at the grapheme-level enables us to increase the amount of correctly aligned data by 40 while maintaining a 7% sentence error rate and 0.8% word error rate. We present the procedure for lightly supervised discriminative training with regard to the objective of minimising sentence error rate.
|Title of host publication||Proc Interspeech 2013|
|Publication status||Published - 1 Aug 2013|
FingerprintDive into the research topics of 'Lightly Supervised Discriminative Training of Grapheme Models for Improved Sentence-level Alignment of Speech and Text Data'. Together they form a unique fingerprint.
- 3 Finished
Simple4All: Speech synthesis that improves through adaptive learning
1/11/11 → 31/10/14
Natural Speech Technology
1/05/11 → 31/07/16
HELP4MOOD:A computational distributed system to support the treatment of patients with major depression
Matheson, C. & Wolters, M.
1/01/11 → 30/06/14
- 1 Invited talk
EACL 2014 keynote: Speech synthesis needs YOU!
Simon King (Speaker)29 Apr 2014
Activity: Academic talk or presentation types › Invited talkFile