Development of a Novel Database in Gujarati Language for Spoken Digits Classification

Dalsaniya, Nikunj; Mankad, Sapan H.; Garg, Sanjay; Shrivastava, Dhuri

doi:10.1007/978-981-15-4828-4_18

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…Additionally, our proposed CNN showed faster convergence, as in Figure 7. For evaluating the effectiveness of our proposed CNN on different languages, we performed the experiments on Gujarati [29] and English [41,42] spoken digits. Our proposed CNN also outperformed the Gujarati Digit Model by absolute 22% accuracy.…”

Section: Classification Resultsmentioning

confidence: 99%

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AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

et al. 2021

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The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical speech recognition system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language of Pakistan and is also widely spoken in many other South Asian countries (e.g., India, Afghanistan). Therefore, we present a comprehensive dataset of spoken Urdu digits ranging from 0 to 9. Our dataset has 25,518 sound samples that are collected from 740 participants. To test the proposed dataset, we apply different existing classification algorithms on the datasets including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and flavors of the EfficientNet. These algorithms serve as a baseline. Furthermore, we propose a convolutional neural network (CNN) for audio digit classification. We conduct the experiment using these networks, and the results show that the proposed CNN is efficient and outperforms the baseline algorithms in terms of classification accuracy.

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Section: Classification Resultsmentioning

confidence: 99%

“…The results are promising, with a 69 percent of overall recognition accuracy. Dalsaniya et al [29] presented a novel, publicly available audio dataset of spoken digits in the Gujarati language, as well as some preliminary results. A comparison of the categorization algorithm with an English language database was also performed.…”

Section: Related Workmentioning

confidence: 99%

AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

et al. 2021

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“…These corpora have been made available to serve a wide range of purposes. (Dalsaniya et al, 2020) introduces an innovative audio dataset comprising isolated Gujarati digits. This database encompasses recordings of digits spoken by 20 individuals from five distinct regions within Gujarat, captured in real-world environments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Free Spoken Digit Gujarati Dataset prompts the conclusion that, following cross-corpus experiments conducted by the research team, there exists substantial potential for enhancing the dataset's capabilities. This enhancement involves exploring diverse features and techniques to augment the system's ability to generalize effectively across various contexts (Dalsaniya et al, 2020).…”

Section: Performance Evaluationmentioning

confidence: 99%

Exploring Speech Corpus For Voice Recognition In Gujarati: An In-Depth Study

Shah,

Kavathiya

2024

eatp

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“…We used 3-layers CNN, EfficientNet [38] and mobileNet [9]. For a fair comparison, we took the same model architectures that were defined in [1,5]. For the CNN model, we implemented a 3-layer architecture as described in [1] and shown in Table 1.…”

Section: Algorithmsmentioning

confidence: 99%

Investigating Multi-feature Selection and Ensembling for Audio Classification

Turab¹,

Kumar²,

Bendechache³

et al. 2022

IJAIA

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Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns–particularly in classification of audio data. For better performance of audio classification, feature selection and combination play a key role as they have the potential to make or break the performance of any DL model. To investigate this role, we conduct an extensive evaluation of the performance of several cutting-edge DL models (i.e., Convolutional Neural Network, EfficientNet, MobileNet, Supper Vector Machine and Multi-Perceptron) with various state-of-the-art audio features (i.e., Mel Spectrogram, Mel Frequency Cepstral Coefficients, and Zero Crossing Rate) either independently or as a combination (i.e., through ensembling) on three different datasets (i.e., Free Spoken Digits Dataset, Audio Urdu Digits Dataset, and Audio Gujarati Digits Dataset). Overall, results suggest feature selection depends on both the dataset and the model. However, feature combinations should be restricted to the only features that already achieve good performances when used individually (i.e., mostly Mel Spectrogram, Mel Frequency Cepstral Coefficients). Such feature combination/ensembling enabled us to outperform the previous state-of-the-art results irrespective of our choice of DL model.

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Development of a Novel Database in Gujarati Language for Spoken Digits Classification

Cited by 10 publications

References 20 publications

AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

Exploring Speech Corpus For Voice Recognition In Gujarati: An In-Depth Study

Investigating Multi-feature Selection and Ensembling for Audio Classification

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