Deep features-based dialect and mood recognition using Assamese telephonic speech
by Mridusmita Sharma; Kandarpa Kumar Sarma
International Journal of Information and Communication Technology (IJICT), Vol. 17, No. 4, 2020

Abstract: Learning aided methods are popular for designing automatic speech recognition (ASR) systems. Majority of works have used shallow models in combination with mel frequency cepstral coefficients (MFCC) and other features for speech recognition applications. Although these shallow models are effective but incorporating deep features in the mechanism for speech processing applications is necessary to increase the efficiency. Despite of considerable amount of works on the design of deep learning topologies and training paradigms in supervised domain, very few works have concentrated on deep features which are essential to capture detailed information of speech. This work focuses on the generation of deep features using stacked auto-encoder for normal and time shifted telephonic speech samples in Assamese language with mood and dialect variations. Experimental results show that the deep features learned by the stacked auto-encoder performs better while it is configured for Assamese speech recognition with mood and dialect variations.

Online publication date: Thu, 29-Oct-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com