Adversarial synthesis based data-augmentation for code-switched spoken language identification

TL;DR

This paper introduces a GAN-based data augmentation method using Mel spectrograms to improve spoken language identification for code-switched Hindi-English speech, addressing data scarcity issues.

Contribution

It proposes a novel GAN-based augmentation technique for code-mixed speech data, enhancing LID accuracy in multilingual scenarios.

Findings

GAN augmentation improves Unweighted Average Recall by 3.5%.

Method effectively models minority class data distribution.

Enhances spoken language identification in code-switched speech.

Abstract

Spoken Language Identification (LID) is an important sub-task of Automatic Speech Recognition(ASR) that is used to classify the language(s) in an audio segment. Automatic LID plays an useful role in multilingual countries. In various countries, identifying a language becomes hard, due to the multilingual scenario where two or more than two languages are mixed together during conversation. Such phenomenon of speech is called as code-mixing or code-switching. This nature is followed not only in India but also in many Asian countries. Such code-mixed data is hard to find, which further reduces the capabilities of the spoken LID. Hence, this work primarily addresses this problem using data augmentation as a solution on the on the data scarcity of the code-switched class. This study focuses on Indic language code-mixed with English. Spoken LID is performed on Hindi, code-mixed with English. This research proposes Generative Adversarial Network (GAN) based data augmentation technique performed using Mel spectrograms for audio data. GANs have already been proven to be accurate in representing the real data distribution in the image domain. Proposed research exploits these capabilities of GANs in speech domains such as speech classification, automatic speech recognition, etc. GANs are trained to generate Mel spectrograms of the minority code-mixed class which are then used to augment data for the classifier. Utilizing GANs give an overall improvement on Unweighted Average Recall by an amount of 3.5% as compared to a Convolutional Recurrent Neural Network (CRNN) classifier used as the baseline reference.