Towards Slovak-English-Mandarin Speech Recognition Using Deep Learning

Pleva, Matúš; Liao, Yuan-Fu; Hsu, Wu-Hua; Hládek, Daniel; Staš, Ján; Viszlay, Peter; Lojka, Martin; Juhar, Jozef

doi:10.23919/elmar.2018.8534661

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Several languages have been influenced such as Arabic-English, Arabic-French [1], Frisian-Dutch [7], Malay-English [8], Cantonese-English [9] , Mandarin-English [10][11][12], Hindi-English [13], Spanish-English , Slovak-English [14], Telugu-English [6], Tamil-English [15], Arabic-Amazigh [16], isiZulu-English [17], as well as Kazakh-Russian [18].…”

Section: Introductionmentioning

confidence: 99%

Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

et al. 2023

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In this technological era, smart and intelligent systems that are integrated with artificial intelligence (AI) techniques, algorithms, tools, and technologies, have impact on various aspects of our daily life. Communication and interaction between human and machine using speech becomes increasingly important, since it is an obvious substitute for keyboards and screens in the communication process. Therefore, numerous technologies take advantage of speech such as Automatic Speech Recognition (ASR), where human natural speech for many languages is used as the mean to interact with machines. Majority of the related works for ASR concentrate on the development and evaluation of ASR systems that serve a single language (monolingual) only, such as Arabic, English, Chinese, French, and many others. However, research attempts that combine multiple languages (bilingual and multilingual) during the development and evaluation of ASR systems are very limited. This paper aims to provide comprehensive research background and fundamentals of bilingual ASR, and related works that have combined two languages for ASR tasks from 2010 through 2021. It also formulates research taxonomy and discusses open challenges to bilingual ASR research. Based on our literature investigation, it is clear that bilingual ASR using deep learning approach is highly demanded and is able to provide acceptable performance. In addition, many combinations of two languages such as Arabic-English, Arabic-Malay, and others, are not attempted yet by the research community, which can open new research opportunities. Finally, it is clear that ASR research is moving towards not only bilingual ASR, but also multilingual ASR.

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Section: Introductionmentioning

confidence: 99%

Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

et al. 2023

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“…In recent years, deep learning methods have caught the eyes from researchers as a tool of representation learning, and have made significant progress in computer vision [17], natural language processing [18], and speech recognition [19]. In the task of HSIc, deep learning methods also have became popular due to their impressive performance [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training

Qin

et al. 2020

Remote Sensing

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Because there are many unlabeled samples in hyperspectral images and the cost of manual labeling is high, this paper adopts semi-supervised learning method to make full use of many unlabeled samples. In addition, those hyperspectral images contain much spectral information and the convolutional neural networks have great ability in representation learning. This paper proposes a novel semi-supervised hyperspectral image classification framework which utilizes self-training to gradually assign highly confident pseudo labels to unlabeled samples by clustering and employs spatial constraints to regulate self-training process. Spatial constraints are introduced to exploit the spatial consistency within the image to correct and re-assign the mistakenly classified pseudo labels. Through the process of self-training, the sample points of high confidence are gradually increase, and they are added to the corresponding semantic classes, which makes semantic constraints gradually enhanced. At the same time, the increase in high confidence pseudo labels also contributes to regional consistency within hyperspectral images, which highlights the role of spatial constraints and improves the HSIc efficiency. Extensive experiments in HSIc demonstrate the effectiveness, robustness, and high accuracy of our approach.

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WITHDRAWN: English vocabulary retrieval and recognition based on FPGA and machine learning

2020

Microprocessors and Microsystems

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Towards Slovak-English-Mandarin Speech Recognition Using Deep Learning

Cited by 3 publications

References 14 publications

Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training

WITHDRAWN: English vocabulary retrieval and recognition based on FPGA and machine learning

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