Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks

TL;DR

This paper introduces a deep Siamese network approach for accent identification that leverages native language speech data, achieving significant accuracy improvements over traditional methods.

Contribution

It presents a novel deep Siamese network model trained on native and accented speech to improve accent identification accuracy.

Findings

15.4% relative performance improvement over baseline

Effective use of native language speech data

Detailed error analysis provided

Abstract

The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems. The accented nature of speech can be primarily attributed to the influence of the speaker's native language on the given speech recording. In this paper, we propose a novel accent identification system whose training exploits speech in native languages along with the accented speech. Specifically, we develop a deep Siamese network-based model which learns the association between accented speech recordings and the native language speech recordings. The Siamese networks are trained with i-vector features extracted from the speech recordings using either an unsupervised Gaussian mixture model (GMM) or a supervised deep neural network (DNN) model. We perform several accent identification experiments using the CSLU Foreign Accented English (FAE) corpus. In these experiments, our proposed approach using deep Siamese networks yield significant relative performance improvements of 15.4 percent on a 10-class accent identification task, over a baseline DNN-based classification system that uses GMM i-vectors. Furthermore, we present a detailed error analysis of the proposed accent identification system.