Speech and Language Processing for Learning and Wellbeing

Helen Meng


Spoken language is a primary form of human communication. Spoken language processing techniques must incorporate knowledge of acoustics, phonetics and linguistics in analyzing speech. While great strides have been made in the community in general speech recognition, reaching human parity in performance, our team has been focusing on the problems of recognizing and analyzing non-native, learners’ speech for the purpose of mispronunciation detection and diagnosis in computer-aided pronunciation training. In order to generate personalized, corrective feedback, we have also developed an approach that uses phonetic posterior-grams (PPGs) for personalized, cross-lingual text-to-speech synthesis given arbitrary textual input, based on voice conversion techniques. We have also extended our work to disordered speech, focusing on automated distinctive feature (DF)-based analyses of dysarthric recordings. The analyses are intended to inform intervention strategies. Additionally, voice conversion is further developed to restore disordered speech to normal speech. This talk will present the challenges in these problems, our approaches and solutions, as well as our ongoing work.


Cite as: Meng, H. (2018) Speech and Language Processing for Learning and Wellbeing. Proc. Interspeech 2018, 3022.


@inproceedings{Meng2018,
  author={Helen Meng},
  title={Speech and Language Processing for Learning and Wellbeing},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={3022}
}