Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal

Mukherjee, Himadri; Obaidullah, Sk Md; Santosh, K. C.; Phadikar, Santanu; Roy, Kaushik

doi:10.1007/s10772-018-9525-6

Cited by 52 publications

(5 citation statements)

References 36 publications

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“…In [12], the VAD method, which uses statistical functions based on the linear spectrum and frequency, was presented. The experiments were conducted on a database of more than 350 h, consisting of data from various sources, such as YouTube.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

The dependence of the effectiveness of neural networks for recognizing human voice on language

Nurlankyzy,

Akhmediyarova,

Zhetpisbayeva

et al. 2024

EEJET

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This study examines the effectiveness of neural network architectures (multilayer perceptron MLP, convolutional neural network CNN, recurrent neural network RNN) for human voice recognition, with an emphasis on the Kazakh language. Problems related to language, the difference between speakers, and the influence of network architecture on recognition accuracy are considered. The methodology includes extensive training and testing, studying the accuracy of recognition in different languages, and different sets of data on speakers. Using a comparative analysis, this study evaluates the performance of three architectures trained exclusively in the Kazakh language. The testing included statements in Kazakhs and other languages, while the number of speakers varied to assess its impact on recognition accuracy. During the study, the results showed that CNN neural networks are more effective in recognizing human voice than RNN and MLP. Also, it was found that the CNN has a higher accuracy in recognizing the human voice in the Kazakh language, both for a small and for a large number of announcers. For example, for 20 speakers, the recognition error in Russian was 21.86 %, whereas in Kazakhs it was 10.6 %. A similar trend was observed for 80 speakers: 16.2 % Russians and 8.3 % Kazakhs. It can also be argued that learning one language does not guarantee high recognition accuracy in other languages. Therefore, the accuracy of human voice recognition by neural networks depends significantly on the language in which training is conducted. In addition, this study highlights the importance of different sets of speaker data to achieve optimal results. This knowledge is crucial for advancing the development of reliable human voice recognition systems that can accurately identify different human voices in different language contexts

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

The dependence of the effectiveness of neural networks for recognizing human voice on language

Nurlankyzy,

Akhmediyarova,

Zhetpisbayeva

et al. 2024

EEJET

View full text Add to dashboard Cite

show abstract

“…The ASR applications are impressive in noise free environment and are not promising solutions for the experiments and tests in noisy areas [13,14,15,16]. Hence, the ASR system throughput is limited by two options.…”

Section: Related Workmentioning

confidence: 99%

Robust Automatic Speech Recognition System for Kannada Speech Sentences in the Presence of Noise

Mahadevaswamy

2022

Preprint

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An Automatic Speech Recognition system is developed for recognizing the continuous and spontaneous Kannada speech sentences. The language models and acoustic models are constructed using Kaldi toolkit. The speech corpus is developed with the native female and male Kannada speakers and is partioned into training set and testing set. The Performance of the proposed system is analysed and evaluated using the metric Word Error Rate (WER). The Wavelet Packets amalgamated with Mel filter banks are utilized to perform feature vector generation. The proposed hand crafted features perform better than the baseline features such as Perceptual Linear Prediction(PLP), Mel Frequency Cepstral Coefficients(MFCC) interms of Word Error Rate(WER) under both controlled and uncontrolled conditions.

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“…From various researches conducted with ELT it is observed that it has some benefits such as less training parameters, improved learning speed and better generalization ability [22]. The improved ELT techniques have been presented in some works and applied for applications like computerized recognition [23], image processing [24] and text recognition [25]. In ELT, the training data can be linearly separated with a hyper plane spreading through the origin having probability one in the ELT feature space.…”

Section: Introductionmentioning

confidence: 99%

Improved Segmentation with Optimization Based Multilevel Thresholding and K-Means Clustering for Plant Disease Identification

David

Gomathi

2022

SSRN Journal

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Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal

Cited by 52 publications

References 36 publications

The dependence of the effectiveness of neural networks for recognizing human voice on language

The dependence of the effectiveness of neural networks for recognizing human voice on language

Robust Automatic Speech Recognition System for Kannada Speech Sentences in the Presence of Noise

Improved Segmentation with Optimization Based Multilevel Thresholding and K-Means Clustering for Plant Disease Identification

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