Kadyan V, Mantri A, Aggarwal RK (2017) A heterogeneous speech feature vectors generation approach with hybrid hmm classifiers. Gumperz JJ (1958) Dialect differences and social stratification in a North Indian Village 1. GuglaniJ MAN (2018) Continuous Punjabi speech recognition model based on Kaldi ASR toolkit. Grzega J (2000) On the description of national varieties: examples from (German and Austrian) German and (English and American) English. In: Proceedings of the 12th language resources and evaluation conference, pp 6458–6461 Gogoi P, Dey A, Lalhminghlui W, Sarmah P, Prasanna SM (2020) Lexical tone recognition in Mizo using acoustic-prosodic features. Įady SJ (1982) Differences in the F0 patterns of speech: tone language versus stress language. Glot Int 5(9/10):341–347ĭua M, Aggarwal RK, KadyanV, Dua S (2012) Punjabi speech to text system for connected words. Appl Acoust 177:107918īoersma P, VanHeuven V (2001) Speak and unSpeak with PRAAT. (96)00024-6īhardwaj V, Kukreja V (2021) Effect of pitch enhancement in Punjabi children’s speech recognition system under disparate acoustic conditions. Int J Comput Sci Eng Inf Technol Res:2249–6831 Īrslan LM, Hansen JH (1996) Language accent classification in American English. Īrora Shipra J, Rishipal S (2014) Acoustic and phonological analysis of homophones of Punjabi language. In: Advances in signal processing and communication. Īrora A, KadyanV, Singh A (2019) Effect of tonal features on various dialectal variations of Punjabi language. Īi OC, Hariharan M, Yaacob S, Chee LS (2012) Classification of speech dysfluencies with MFCC and LPCC features. J Acoust Soc Am 121(2):1130–1141Īgrawal SS, Jain A, Sinha S (2016) Analysis and modeling of acoustic information for automatic dialect classification. Īdank P, Van Hout R, Velde HVD (2007) An acoustic description of the vowels of northern and southern standard Dutch II: regional varieties. The result shows that the hybrid LPCC + F0 system achieved a Relative Improvement (R.I.) of 6.94% on Subspace Gaussian Mixture Model model in comparison to that of basic LPCC approach respectively.Īdank P, Van Hout R, Smits R (2004) An acoustic description of the vowels of Northern and Southern Standard Dutch. Further work is extended through processing of acoustic information at feature level or by comparing the performance analysis using basic or hybrid Linear Predictive Cepstral Coefficients feature extraction methods. Apart F1 and F2 have shown a significant correlation with each spoken dialect. The results analysis showed that the fundamental frequency of these vowels are influenced distinctly in different dialectal conditions. Each variable has been compared with same variable of all other dialects. The speech analysis tool PRAAT features have been extracted and correlations are studied using Statistical Package for the Social Sciences (SPSS).
The results are based on four different dialects which provide us some interesting hypotheses and are explored with self-created dataset. This is an empirical study of sound words in four major dialects of Indian Punjabi language with two key parameters, namely F0 variation, and acoustic space, which are calculated using two formant frequencies: F1, and F2. In this paper, an issue of dialect classification is perform on the basis of tonal aspects of laryngeal phoneme. In the field of Automatic Speech Recognition (ASR), key challenge is to recognize and to generate an acoustic model which represents differences of redundant acoustic features. The major reason behind variability in some language is due to varying dialect of the speakers. Human beings have their own speaking style which helped them in depicting their native language.