Yun Zhang, Zhenhua Yu^*, Xufei Hu, Xuya Cong, Ou Ye

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5403-5426, 2025, DOI:10.32604/cmc.2025.067120 - 30 July 2025

Abstract Devices in Industrial Internet of Things are vulnerable to voice adversarial attacks. Studying adversarial speech samples is crucial for enhancing the security of automatic speech recognition systems in Industrial Internet of Things devices. Current black-box attack methods often face challenges such as complex search processes and excessive perturbation generation. To address these issues, this paper proposes a black-box voice adversarial attack method based on enhanced neural predictors. This method searches for minimal perturbations in the perturbation space, employing an optimization process guided by a self-attention neural predictor to identify the optimal perturbation direction. This direction… More >

Chiara Innocente^1,*, Matteo Boemio², Gianmarco Lorenzetti², Ilaria Pulito², Diego Romagnoli², Valeria Saponaro², Giorgia Marullo¹, Luca Ulrich¹, Enrico Vezzetti¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1355-1379, 2025, DOI:10.32604/cmes.2025.063186 - 30 May 2025

Abstract Lip-reading technology, based on visual speech decoding and automatic speech recognition, offers a promising solution to overcoming communication barriers, particularly for individuals with temporary or permanent speech impairments. However, most Visual Speech Recognition (VSR) research has primarily focused on the English language and general-purpose applications, limiting its practical applicability in medical and rehabilitative settings. This study introduces the first Deep Learning (DL) based lip-reading system for the Italian language designed to assist individuals with vocal cord pathologies in daily interactions, facilitating communication for patients recovering from vocal cord surgeries, whether temporarily or permanently impaired. To… More >

Shuting Ge^1,2, Jin Ren^2,3,*, Yihua Shi⁴, Yujun Zhang¹, Shunzhi Yang², Jinfeng Yang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746 - 26 March 2024

Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues,… More >

Soonshin Seo^1,2, Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2833-2856, 2023, DOI:10.32604/cmc.2023.042816 - 26 December 2023

Abstract Automatic speech recognition (ASR) systems have emerged as indispensable tools across a wide spectrum of applications, ranging from transcription services to voice-activated assistants. To enhance the performance of these systems, it is important to deploy efficient models capable of adapting to diverse deployment conditions. In recent years, on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios. However, these methods often confront substantial trade-offs, particularly in terms of unstable accuracy when reducing the model size. To address challenges, this study introduces two crucial empirical findings. Firstly,… More >

Peiyuan Jiang¹, Weijun Pan^1,*, Jian Zhang¹, Teng Wang¹, Junxiang Huang²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 911-940, 2023, DOI:10.32604/cmc.2023.041772 - 31 October 2023

Abstract

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition (ASR) technology in the Air Traffic Control (ATC) field. This paper presents a novel cascaded model architecture, namely Conformer-CTC/Attention-T5 (CCAT), to build a highly accurate and robust ATC speech recognition model. To tackle the challenges posed by noise and fast speech rate in ATC, the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms. On the decoding side, the Attention mechanism is integrated to facilitate precise alignment between input features and

… More >

Wen-Tsai Sung¹, Hao-Wei Kang¹, Sung-Jung Hsiao^2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3833-3858, 2023, DOI:10.32604/cmc.2023.040024 - 08 October 2023

Abstract In the speech recognition system, the acoustic model is an important underlying model, and its accuracy directly affects the performance of the entire system. This paper introduces the construction and training process of the acoustic model in detail and studies the Connectionist temporal classification (CTC) algorithm, which plays an important role in the end-to-end framework, established a convolutional neural network (CNN) combined with an acoustic model of Connectionist temporal classification to improve the accuracy of speech recognition. This study uses a sound sensor, ReSpeaker Mic Array v2.0.1, to convert the collected speech signals into text… More >

Nada Faisal Aljohani^*, Emad Sami Jaha

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3037-3058, 2023, DOI:10.32604/csse.2023.037113 - 03 April 2023

Abstract The continuing advances in deep learning have paved the way for several challenging ideas. One such idea is visual lip-reading, which has recently drawn many research interests. Lip-reading, often referred to as visual speech recognition, is the ability to understand and predict spoken speech based solely on lip movements without using sounds. Due to the lack of research studies on visual speech recognition for the Arabic language in general, and its absence in the Quranic research, this research aims to fill this gap. This paper introduces a new publicly available Arabic lip-reading dataset containing 10490… More >

Sung-Jung Hsiao¹, Wen-Tsai Sung^2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3817-3832, 2023, DOI:10.32604/cmc.2023.037380 - 31 March 2023

Abstract Speech plays an extremely important role in social activities. Many individuals suffer from a “speech barrier,” which limits their communication with others. In this study, an improved speech recognition method is proposed that addresses the needs of speech-impaired and deaf individuals. A basic improved connectionist temporal classification convolutional neural network (CTC-CNN) architecture acoustic model was constructed by combining a speech database with a deep neural network. Acoustic sensors were used to convert the collected voice signals into text or corresponding voice signals to improve communication. The method can be extended to modern artificial intelligence techniques, More >

Mohamad Khairi Ishak¹, Dag Øivind Madsen^2,*, Fahad Ahmed Al-Zahrani³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1951-1961, 2023, DOI:10.32604/iasc.2023.033971 - 05 January 2023

Abstract Natural language processing technologies have become more widely available in recent years, making them more useful in everyday situations. Machine learning systems that employ accessible datasets and corporate work to serve the whole spectrum of problems addressed in computational linguistics have lately yielded a number of promising breakthroughs. These methods were particularly advantageous for regional languages, as they were provided with cutting-edge language processing tools as soon as the requisite corporate information was generated. The bulk of modern people are unconcerned about the importance of reading. Reading aloud, on the other hand, is an effective… More >

Mohammed Hadwan^1,2,*, Hamzah A. Alsayadi^3,4, Salah AL-Hagree⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3471-3487, 2023, DOI:10.32604/cmc.2023.033457 - 31 October 2022

Abstract The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-to-end transformer-based models for the Arabic language, as the researchers’ community pays little attention to it. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur’an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and… More >