# Attention model for articulatory features detection

arXiv: https://arxiv.org/abs/1907.01914 · HTML: https://mlsanity.com/paper/1907.01914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method](https://mlsanity.com/paper/2311.07037/) (2023)

## Abstract (arXiv)

Articulatory distinctive features, as well as phonetic transcription, play important role in speech-related tasks: computer-assisted pronunciation training, text-to-speech conversion (TTS), studying speech production mechanisms, speech recognition for low-resourced languages. End-to-end approaches to speech-related tasks got a lot of traction in recent years. We apply Listen, Attend and Spell~(LAS)~citeChan-LAS2016 architecture to phones recognition on a small small training set, like TIMIT~citeTIMIT-1992. Also, we introduce a novel decoding technique that allows to train manners and places of articulation detectors end-to-end using attention models. We also explore joint phones recognition and articulatory features detection in multitask learning setting.