# Flowchase: a Mobile Application for Pronunciation Training

arXiv: https://arxiv.org/abs/2307.02051 · HTML: https://mlsanity.com/paper/2307.02051/

## 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 (most cited first, 8 of 8)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment](https://mlsanity.com/paper/2205.03432/) (2022)
- [Transfer Learning for Speech and Language Processing](https://mlsanity.com/paper/1511.06066/) (2015)
- [Exploring Transfer Learning for Low Resource Emotional TTS](https://mlsanity.com/paper/1901.04276/) (2019)
- [Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis](https://mlsanity.com/paper/1903.11570/) (2019)
- [ASR-based Features for Emotion Recognition: A Transfer Learning Approach](https://mlsanity.com/paper/1805.09197/) (2018)

## Built on by (most cited first, 2 of 2)

- [MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and Phonetic Domains for Speech Representation Learning](https://mlsanity.com/paper/2310.11541/) (2023)
- [TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer](https://mlsanity.com/paper/2405.02124/) (2024)

## 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.