RETRACTED: Real time speech recognition algorithm on embedded system based on continuous Markov model

He, Yu; Dong, Xiguang

doi:10.1016/j.micpro.2020.103058

Cited by 15 publications

(2 citation statements)

References 17 publications

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“…The recognition of speech in Indian regional languages is a broadly concerned field because of the complexities in such languages. The advancements in technologies have emerged over the country without the bondage of language, which can be realized using the speech recognition model with the regional languages [2]. Speech recognition is generally continued by the vocabulary that insists on operating the regularly utilized words.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Bachate¹,

Sharma²,

Singh³

et al. 2022

Computer Systems Science and Engineering

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Communication is a significant part of being human and living in the world. Diverse kinds of languages and their variations are there; thus, one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent. Numerous application fields such as education, mobility, smart systems, security, and health care systems utilize the speech or voice recognition models abundantly. Though, various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages. It is necessary to understand the speech recognition field, in which the major concentrated stages are feature extraction and classification. This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network (RNN). Here, the preprocessing of the input signal is performed by smoothing and median filtering. After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus. The optimized RNN classifier is used for speech recognition after completing the feature extraction task, where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm (GOA). Finally, the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

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Section: Introductionmentioning

confidence: 99%

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Bachate¹,

Sharma²,

Singh³

et al. 2022

Computer Systems Science and Engineering

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show abstract

“…It is evident that the more complex the algorithm, the more time it takes to perform the analysis [ 6 ], resulting in delays. Due to the complex nature of the Lombard effect [ 7 ], deep learning techniques seem more suitable than baseline algorithms, being successfully applied in real-time speech recognition systems [ 8 , 9 ]. Therefore, in our work, we propose deep neural network models that are relatively simple.…”

Section: Introductionmentioning

confidence: 99%

Detecting Lombard Speech Using Deep Learning Approach

Kąkol¹,

Korvel

Tamulevičius

et al. 2022

Sensors

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Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks (CNNs) and various two-dimensional (2D) speech signal representations. To reduce the computational cost and not resign from the 2D representation-based approach, a strategy for threshold-based averaging of the Lombard effect detection results is introduced. The pseudocode of the averaging process is also included. A series of experiments are performed to determine the most effective network structure and the 2D speech signal representation. Investigations are carried out on German and Polish recordings containing Lombard speech. All 2D signal speech representations are tested with and without augmentation. Augmentation means using the alpha channel to store additional data: gender of the speaker, F0 frequency, and first two MFCCs. The experimental results show that Lombard and neutral speech recordings can clearly be discerned, which is done with high detection accuracy. It is also demonstrated that the proposed speech detection process is capable of working in near real-time. These are the key contributions of this work.

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Smart Home Security Solutions using Facial Authentication and Speaker Recognition through Artificial Neural Networks

Saxena

Varshney

2021

International Journal of Cognitive Computing in Engineering

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RETRACTED: Real time speech recognition algorithm on embedded system based on continuous Markov model

Cited by 15 publications

References 17 publications

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Detecting Lombard Speech Using Deep Learning Approach

Smart Home Security Solutions using Facial Authentication and Speaker Recognition through Artificial Neural Networks

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