Long short-term memory recurrent neural network-based acoustic model using connectionist temporal classification on a large-scale training corpus

Lee, Dong-Hyun; Lim, Minkyu; Park, Hosung; Kang, Yoseb; Park, Jeong-Sik; Jang, Gil-Jin; Kim, Ji‐Hwan

doi:10.1109/cc.2017.8068761

Cited by 54 publications

(19 citation statements)

References 15 publications

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“…This organization makes RNNs memory-centric in operation since they have to store weight synapses in available memory. A type of RNN referred to as Long Short Term Memory (LSTM) is gaining interest in recent times and process to be more effective than conventional RNNs [ 117 , 118 , 119 , 120 ]. Khan et al [ 121 ] developed a data analytic framework with the real-world application using Spark Machine Learning and LSTM techniques.…”

Section: Embedded Machine Learning Techniquesmentioning

confidence: 99%

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

Ajani

Imoize

Atayero

2021

Sensors

105

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Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.

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Section: Embedded Machine Learning Techniquesmentioning

confidence: 99%

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

Ajani

Imoize

Atayero

2021

Sensors

105

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“…Due to the large amounts of information during the past two decades, there emerged a need for technical methods of classifying huge texts as statistical and manual methods had never become useless in this field [35][36][37][38][39][40][41][42][43][44][45][46][47][48]. Therefore, machine learning classification techniques appeared which aim to classify unstructured texts and documents based on certain algorithms designed for this purpose.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…As for the output port, it controls the output after the activation process, which takes place at the entry gate. A portal called the forget gate, which addresses the weakness of LSTM in determining flows for specific units, has also been added [46]. Figure 6 shows the structure of LSTM RNN.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

A systematic review of text classification research based on deep learning models in Arabic language

Wahdan

Hantoobi

Salloum

et al. 2020

IJECE

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Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

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“…Recent advances in deep learning techniques realized continuous speech recognition at stable performance [8][9][10]. However, the high computational intensity of deep learning algorithms requires high-performance hardware.…”

Section: Conventional Speech Recognition Schemesmentioning

confidence: 99%

Front-End of Vehicle-Embedded Speech Recognition for Voice-Driven Multi-UAVs Control

Park

2020

Applied Sciences

Self Cite

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For reliable speech recognition, it is necessary to handle the usage environments. In this study, we target voice-driven multi-unmanned aerial vehicles (UAVs) control. Although many studies have introduced several systems for voice-driven UAV control, most have focused on a general speech recognition architecture to control a single UAV. However, for stable voice-controlled driving, it is essential to handle the environmental conditions of UAVs carefully, including environmental noise that deteriorates recognition accuracy, and the operating scheme, e.g., how to direct a target vehicle among multiple UAVs and switch targets using speech commands. To handle these issues, we propose an efficient, vehicle-embedded speech recognition front-end for multi-UAV control via voice. First, we propose a noise reduction approach that considers non-stationary noise in outdoor environments. The proposed method improves the conventional minimum mean squared error (MMSE) approach to handle non-stationary noises, e.g., babble and vehicle noises. In addition, we propose a multi-channel voice trigger method that can control multiple UAVs while efficiently directing and switching the target vehicle via speech commands. We evaluated the proposed methods on speech corpora, and the experimental results demonstrate that the proposed methods outperform the conventional approaches. In trigger word detection experiments, our approach yielded approximately 7%, 12%, and 3% relative improvements over spectral subtraction, adaptive comb filtering, and the conventional MMSE, respectively. In addition, the proposed multi-channel voice trigger approach achieved approximately 51% relative improvement over the conventional approach based on a single trigger word.

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Long short-term memory recurrent neural network-based acoustic model using connectionist temporal classification on a large-scale training corpus

Cited by 54 publications

References 15 publications

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

A systematic review of text classification research based on deep learning models in Arabic language

Front-End of Vehicle-Embedded Speech Recognition for Voice-Driven Multi-UAVs Control

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