  We propose a novel language-universal approach to end-to-end automatic spoken
keyword recognition (SKR) leveraging upon (i) a self-supervised pre-trained
model, and (ii) a set of universal speech attributes (manner and place of
articulation). Specifically, Wav2Vec2.0 is used to generate robust speech
representations, followed by a linear output layer to produce attribute
sequences. A non-trainable pronunciation model then maps sequences of
attributes into spoken keywords in a multilingual setting. Experiments on the
Multilingual Spoken Words Corpus show comparable performances to character- and
phoneme-based SKR in seen languages. The inclusion of domain adversarial
training (DAT) improves the proposed framework, outperforming both character-
and phoneme-based SKR approaches with 13.73% and 17.22% relative word error
rate (WER) reduction in seen languages, and achieves 32.14% and 19.92% WER
reduction for unseen languages in zero-shot settings.
