Accent Recognition with Hybrid Phonetic Features
This work addresses robustness in voice-controlled systems for users with accents, but it is incremental as it builds on existing methods with specific gains.
The paper tackled the problem of accent recognition for voice-controlled systems by using a hybrid structure with phonetic features from ASR, achieving a 6.57% relative improvement on a validation set and 7.28% on a test set.
The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, the frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with the language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the Accented English Speech Recognition Challenge (AESRC) 2020 dataset. The results demonstrate that our approach can obtain a 6.57% relative improvement on the validation set. We also get a 7.28% relative improvement on the final test set for this competition, showing the merits of the proposed method.