Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
Search Results
Item Robust Dialect Identification System using Spectro-Temporal Gabor Features(Institute of Electrical and Electronics Engineers Inc., 2018) Chittaragi, N.B.; Mothukuri, S.P.; Hegde, P.; Koolagudi, S.G.Automatic identification of dialects of a language is gaining popularity in the field of automatic speech recognition (ASR) systems. The present work proposes an automatic dialect identification (ADI) system using 2D Gabor and spectral features. A comprehensive study of the five dialects of a Dravidian Kannada language has been taken up. Gabor filters representing spectro-temporal modulations attempt in emulation of the human auditory system concerning signal processing strategies. Hence, they are able to well perceive human voices in tern recognize dialectal variations effectively. Also, spectral features Mel frequency cepstral coefficients (MFCC) are derived. A single classifier based support vector machine (SVM) and ensemble based extreme random forest (ERF) classification methods are employed for recognition. The effectiveness of the Gabor features for ADI system is demonstrated with proposed Kannada dialect dataset along with a standard intonation variation in English (IViE) dataset for British English dialects. The Gabor features have shown better performance over MFCC features with both datasets. Better recognition performance of 88.75% and 99.16% is achieved with Kannada and IViE dialect datasets respectively. Proposed Gabor features have demonstrated better performances even under noisy conditions. © 2018 IEEE.Item Dialect Recognition System Using Excitation Source Features(Institute of Electrical and Electronics Engineers Inc., 2018) Choudhury, A.R.; Chittaragi, N.B.; Koolagudi, S.G.This paper focuses on building an automatic dialect recognition system using excitation source features. Every spoken unit represents the unique articulatory configuration of the excitation source and the vocal tract system. This paper emphasis on exploring source information to capture dialectal cues over vocal tract information. Epochs representing the instants of maximum excitation of the vocal tract at the closure are used as source features. Additionally, strength and slope of epochs and instantaneous frequency features are extracted from zero frequency filtered signal. Further, 13 cepstral coefficients are derived from the LP residual to prepare feature vector. Two dialect datasets such as Kannada dataset with five prominent dialects and English dataset with nine dialects are used for evaluation of the significances explored features. Classification experiments are conducted with support vector machines designed with sequential minimal optimization (SMO-SVM) function. Performances are analyzed individually and in combinations. Obtained results have exhibited the existence of dialect information at excitation source information and complementary cues at vocal tract system. © 2018 IEEE.