Formants and LPC Analysis of Kannada Vowel Speech Signals

Chakrasali, Sadashiva V.; Bilembagi, Umesh; Indira, K.

doi:10.1109/rteict42901.2018.9012641

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Agrawal et al presented an acoustic analysis for Hindi vowels for native and non-native speakers [1]. Chakrasali et al presented formant analysis for Kannada vowels speech signals [5], Loakes et al presented acoustic-phonetic analysis for the Wunambal language in Australia [9], Roux and Holtzhausen for the Xhosa Language in Africa [14] and Hillenbrand et al for the American English [7].…”

Section: Related Workmentioning

confidence: 99%

Acoustic Analysis of Vowels in Konkani

Swapnil

Fernandes

Karmali

et al. 2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Konkani is an under-resourced language mainly spoken on the west coast of India. Although linguistic analyses of vowel sounds in various dialects of Konkani have been done in the past, more accurate analysis of Konkani vowels, especially an acoustic-phonetic analysis, was never carried out. In this article, we present a detailed analysis of nine Konkani vowels, namely /i/, /e/, /ε/, /u/, /o/, /ɔ/, /a/, /ə/, and /ɨ̞/ . The dataset used for the analysis was created from audio recordings of 28 native speakers of Goan Konkani. Based on the experimental results, we propose a vowel chart for Konkani. We also observed a partial loss of Konkani vowel / ɨ̞ / in the regular speech of native speakers. This change is also evident in the substitution analysis of vowel phonemes that was carried out by us as a part of this study.

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Section: Related Workmentioning

confidence: 99%

Acoustic Analysis of Vowels in Konkani

Swapnil

Fernandes

Karmali

et al. 2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

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Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features

Gong

Chao

et al. 2021

PLoS ONE

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The research describes the recognition and classification of the acoustic characteristics of amphibians using deep learning of deep neural network (DNN) and long short-term memory (LSTM) for biological applications. First, original data is collected from 32 species of frogs and 3 species of toads commonly found in Taiwan. Secondly, two digital filtering algorithms, linear predictive coding (LPC) and Mel-frequency cepstral coefficient (MFCC), are respectively used to collect amphibian bioacoustic features and construct the datasets. In addition, principal component analysis (PCA) algorithm is applied to achieve dimensional reduction of the training model datasets. Next, the classification of amphibian bioacoustic features is accomplished through the use of DNN and LSTM. The Pytorch platform with a GPU processor (NVIDIA GeForce GTX 1050 Ti) realizes the calculation and recognition of the acoustic feature classification results. Based on above-mentioned two algorithms, the sound feature datasets are classified and effectively summarized in several classification result tables and graphs for presentation. The results of the classification experiment of the different features of bioacoustics are verified and discussed in detail. This research seeks to extract the optimal combination of the best recognition and classification algorithms in all experimental processes.

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Formants and LPC Analysis of Kannada Vowel Speech Signals

Cited by 2 publications

References 2 publications

Acoustic Analysis of Vowels in Konkani

Acoustic Analysis of Vowels in Konkani

Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features

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