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We propose a first step toward multilingual end-to-end automatic speechrecognition (ASR) by integrating knowledge about speech articulators. The keyidea is to leverage a rich set of fundamental units that can be defined"universally" across all spoken languages, referred to as speech attributes,namely manner and place of articulation. Specifically, several deterministicattribute-to-phoneme mapping matrices are constructed based on the predefinedset of universal attribute inventory, which projects the knowledge-richarticulatory attribute logits, into output phoneme logits. The mapping putsknowledge-based constraints to limit inconsistency with acoustic-phoneticevidence in the integrated prediction. Combined with phoneme recognition, ourphone recognizer is able to infer from both attribute and phoneme information.The proposed joint multilingual model is evaluated through phoneme recognition.In multilingual experiments over 6 languages on benchmark datasets LibriSpeechand CommonVoice, we find that our proposed solution outperforms conventionalmultilingual approaches with a relative improvement of 6.85% on average, and italso demonstrates a much better performance compared to monolingual model.Further analysis conclusively demonstrates that the proposed solutioneliminates phoneme predictions that are inconsistent with attributes.

Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints