  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)~\cite{Chan-LAS2016} architecture to phones
recognition on a small small training set, like TIMIT~\cite{TIMIT-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.
