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The performance of voice-controlled systems is usually influenced by accentedspeech. To make these systems more robust, the frontend accent recognition (AR)technologies have received increased attention in recent years. As accent is ahigh-level abstract feature that has a profound relationship with the languageknowledge, AR is more challenging than other language-agnostic audioclassification tasks. In this paper, we use an auxiliary automatic speechrecognition (ASR) task to extract language-related phonetic features.Furthermore, we propose a hybrid structure that incorporates the embeddings ofboth a fixed acoustic model and a trainable acoustic model, making thelanguage-related acoustic feature more robust. We conduct several experimentson the Accented English Speech Recognition Challenge (AESRC) 2020 dataset. Theresults demonstrate that our approach can obtain a 6.57% relative improvementon the validation set. We also get a 7.28% relative improvement on the finaltest set for this competition, showing the merits of the proposed method.

Accent Recognition with Hybrid Phonetic Features