Acoustic Features Based Accent Classification of Kashmiri Language using Deep Learning

Acoustic Features Based Accent Classification of Kashmiri Language using Deep Learning

Shehzen Sidiq Malla

Contact

Acoustic Features Based Accent Classification of Kashmiri Language using Deep Learning

Article Fingerprint

ReserarchID

2NNHZ

Acoustic Features Based Accent Classification of Kashmiri Language using Deep Learning Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

Automatic identification of accents is important in today’s world, where we are souranded by ASR systems. Accent classification is the problem of knowing the native place of a person from the way He/She speaks the language into consideration. Accents are present in almost all the languages and it forms an important part of the language. Accents are produced from prosodic and articulation characteristics; in this research the aim is to classify accents of Kashmir Language. We have considered using the MFCC and Mel spectrograms for our research. A lot of research has been done for languages like English and is being done in this field and many models of machine learning and deep learning have shown state of the art results, but this problem is new for Kashmiri Language. The accents in Kashmir, vary from area to area and we have chosen 6 areas as our classes. We extracted the features from the audio data, converted those features into Images and then used the CNN architectures as our model. This research can be taken as base research for further researches in this language. Our custom models achieved the loss of 0.12 and accuracy of 98.66% on test data using Mel spectrograms, which is our best for our features.

References

21 Cites in Article

Reference Format

L Kat,P Fung (1999). Fast accent identification and accented speech recognition.
C Huang,T Chen,E Chang (2004). Accent issues in large vocabulary continuous speech recognition.
D Tanner,M Tanner (2004). Gosford Park – The Company – Tanner on Tanner.
Fadi Biadsy,Julia Hirschberg,Daniel Ellis (2011). Dialect and accent recognition using phonetic-segmentation supervectors.
S Deshpande,S Chikkerur,V Govindaraju (2005). Accent Classification in Speech.
T Chen,C Huang,E Chang,J Wang (2001). Automatic accent identification using gaussian mixture models.
Hong Tang,Ali Ghorbani (2003). Accent Classification Using Support Vector Machine and Hidden Markov Model.
Karsten Kumpf,Robin King (1997). Foreign speaker accent classification using phoneme-dependent accent discrimination models and comparisons with human perception benchmarks.
Geoffrey Hinton,Li Deng,Dong Yu,George Dahl,Abdel-Rahman Mohamed,Navdeep Jaitly,Andrew Senior,Vincent Vanhoucke,Patrick Nguyen,Tara Sainath,Brian Kingsbury (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups.
H Zen,H Sak (2015). Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis.
Yong Xu,Jun Du,Li-Rong Dai,Chin-Hui Lee (2014). An Experimental Study on Speech Enhancement Based on Deep Neural Networks.
Yishan Jiao,Ming Tu,Visar Berisha,Julie Liss (2016). Online speaking rate estimation using recurrent neural networks.
M Chan,Xin Feng,J Heinen,R Niederjohn (1994). Classification of speech accents with neural networks.
S Rabiee,Setayeshi (2010). Persian accents identification using an adaptive neural network.
G Montavon (2009). Deep learning for spoken language identification.
R Cole,J Inouye,Y Muthusamy,M Gopalakrishnan (1989). Language identification with neural networks: a feasibility study.
Ignacio Lopez-Moreno,Javier Gonzalez-Dominguez,Oldrich Plchot,David Martinez,Joaquin Gonzalez-Rodriguez,Pedro Moreno (2014). Automatic language identification using deep neural networks.
Yuni Zeng,Hua Mao,Dezhong Peng,Zhang Yi (2019). Spectrogram based multi-task audio classification.
D Park (2019). SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.
Huimin Zhao,Xianglin Huang,Wei Liu,Lifang Yang (2018). Environmental sound classification based on feature fusion.
B Mcfee (2015). librosa: Audio and Music Signal Analysis in Python.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Shehzen Sidiq Malla. 2026. \u201cAcoustic Features Based Accent Classification of Kashmiri Language using Deep Learning\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 22 (GJCST Volume 22 Issue D1): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJCST Volume 22 Issue D1
Pg. 39- 43

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-D Classification: I.2.7

Submission ReceivedDecember 5, 2021
Peer Review Double Blind
Handling Editor
Accepted December 29, 2021
Published January 9, 2022

Version of record

v1.2

Issue date

January 22, 2022

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Views: 3180

Total Downloads: 51

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]