Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks

12/25/2017
by   Aditya Siddhant, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/09/2018

Native Language Identification using i-vector

The task of determining a speaker's native language based only on his sp...
research
10/18/2022

Risk of re-identification for shared clinical speech recordings

Large, curated datasets are required to leverage speech-based tools in h...
research
10/11/2018

Novel Cascaded Gaussian Mixture Model-Deep Neural Network Classifier for Speaker Identification in Emotional Talking Environments

This research is an effort to present an effective approach to enhance t...
research
05/30/2023

Investigating model performance in language identification: beyond simple error statistics

Language development experts need tools that can automatically identify ...
research
07/28/2020

Siamese x-vector reconstruction for domain adapted speaker recognition

With the rise of voice-activated applications, the need for speaker reco...
research
12/02/2021

Computing Class Hierarchies from Classifiers

A class or taxonomic hierarchy is often manually constructed, and part o...
research
11/04/2014

Tied Probabilistic Linear Discriminant Analysis for Speech Recognition

Acoustic models using probabilistic linear discriminant analysis (PLDA) ...

Please sign up or login with your details

Forgot password? Click here to reset