Accent Recognition with Hybrid Phonetic Features

• Main Authors: Zhan Zhang, Yuehai Wang, Jianyi Yang
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Sensors
• Subjects: accent recognition; audio classification; accented English speech recognition
• Online Access: https://www.mdpi.com/1424-8220/21/18/6258

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, 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 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 AESRC dataset. The results demonstrate that our approach can obtain an 8.02% relative improvement compared with the Transformer baseline, showing the merits of the proposed method.