Multiclass Language Identification using Deep Learning on Spectral Images of Audio Signals

Abstract

The first step in any voice recognition software is to determine what language a speaker is using, and ideally this process would be automated. The technique described in this paper, language identification for audio spectrograms (LIFAS), uses spectrograms generated from audio signals as inputs to a convolutional neural network (CNN) to be used for language identification. LIFAS requires minimal pre-processing on the audio signals as the spectrograms are generated during each batch as they are input to the network during training. LIFAS utilizes deep learning tools that are shown to be successful on image processing tasks and applies it to audio signal classification. LIFAS performs binary language classification with an accuracy of 97\%, and multi-class classification with six languages at an accuracy of 89\% on 3.75 second audio clips.

References in corpus (2)

• SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
• A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay

Cited by in corpus (7)

• fastai: A Layered API for Deep Learning
• Towards Learning a Universal Non-Semantic Representation of Speech
• LEAF: A Learnable Frontend for Audio Classification
• Spoken Language Identification using ConvNets
• An Investigation of the Effectiveness of Phase for Audio Classification
• Is Attention always needed? A Case Study on Language Identification from Speech
• What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions