Flowchase: a Mobile Application for Pronunciation Training
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Abstract (arXiv)
In this paper, we present a solution for providing personalized and instant feedback to English learners through a mobile application, called Flowchase, that is connected to a speech technology able to segment and analyze speech segmental and supra-segmental features. The speech processing pipeline receives linguistic information corresponding to an utterance to analyze along with a speech sample. After validation of the speech sample, a joint forced-alignment and phonetic recognition is performed thanks to a combination of machine learning models based on speech representation learning that provides necessary information for designing a feedback on a series of segmental and supra-segmental pronunciation aspects.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 5 Jul 2023.
Builds on
8 of 13 references.
References · 8
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2018A Survey on Deep Transfer Learningcited by 419
- 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 115
- 2022Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessmentcited by 37
- 2015Transfer Learning for Speech and Language Processingcited by 35
- 2019Exploring Transfer Learning for Low Resource Emotional TTScited by 21
- 2019Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysiscited by 8
- 2018ASR-based Features for Emotion Recognition: A Transfer Learning Approachcited by 6
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.