Regional Language Recognition System for Industry 4.0
30 Jul 2021-pp 35-53
About: The article was published on 2021-07-30. It has received None citations till now. The article focuses on the topics: Regional language & Tamil.
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TL;DR: The Industry 4.0 environment is scanned on this paper, describing the so-called enabling technologies and systems over the manufacturing environment.
586 citations
TL;DR: A human-centric empowering technology: industrial wearable system is proposed to establish a human–cyber–physical symbiosis to support real time, trusting, and dynamic interaction among operators, machines and production systems.
Abstract: The Industry 4.0 program and corresponding international initiatives continue to transform the industrial workforce and their work. The service-oriented, customer-centric and demand-driven production is pushing forward the progress of industrial automation. Even though, it does not mean that human can be fully replaced by machines/robots. There is an increasing awareness that human presence is not only one type of manufacturing capability, but also contributes to the overall system’s fault tolerant. How to achieve the seamless integration between human and machines/robots and harness human’s full potential is a critical issue for the success of Industry 4.0. In this research, a human-centric empowering technology: industrial wearable system is proposed. The aim of this system is to establish a human–cyber–physical symbiosis to support real time, trusting, and dynamic interaction among operators, machines and production systems. In order to design a substantial framework, three world-leading R&D groups in this field are investigated. Five design considerations have been identified from real-life pilot projects. The future trends and research opportunities also show great promise of industrial wearable system in the next generation of manufacturing.
109 citations
01 Jan 2011
TL;DR: Implementation of speech recognition system on a mobile robot for controlling movement of the robot is described and the highest recognition rate that can be achieved is 91.4%.
Abstract: This paper describes about implementation of speech recognition system on a mobile robot for controlling movement of the robot. The methods used for speech recognition system are Linear Predictive Coding (LPC) and Artificial Neural Network (ANN). LPC method is used for extracting feature of a voice signal and ANN is used as the recognition method. Backpropagation method is used to train the ANN. Voice signals are sampled directly from the microphone and then they are processed using LPC method for extracting the features of voice signal. For each voice signal, LPC method produces 576 data. Then, these data become the input of the ANN. The ANN was trained by using 210 data training. This data training includes the pronunciation of the seven words used as the command, which are created from 30 different people. Experimental results show that the highest recognition rate that can be achieved by this system is 91.4%. This result is obtained by using 25 samples per word, 1 hidden layer, 5 neurons for each hidden layer, and learning rate 0.1.
65 citations
Journal Article•
TL;DR: A Hidden Markov Model (HMM) based word and triphone acoustic models for medium and large vocabulary continuous speech recognizers for Tamil language are attempted.
Abstract: Building a continuous speech recognizer for the Indian language like Tamil is a challenging task due to the unique inherent features of the language like long and short vowels, lack of aspirated stops, aspirated consonants and many instances of allophones. Stress and accent vary in spoken Tamil language from region to region. But in formal read Tamil speech, stress and accents are ignored. There are three approaches to continuous speech recognition (CSR) based on the sub-word unit viz. word, phoneme and syllable. Like other Indian languages, Tamil is also syllabic in nature. Pronunciation of words and sentences is strictly governed by set of linguistic rules. Many attempts have been made to build continuous speech recognizers for Tamil for small and restricted tasks. However medium and large vocabulary CSR for Tamil is relatively new and not explored. In this paper, the authors have attempted to build a Hidden Markov Model (HMM) based word and triphone acoustic models. The objective of this research is to build a small vocabulary word based and a medium vocabulary triphone based continuous speech recognizers for Tamil language. In this experimentation, a word based Context Independent (CI) acoustic model for 371 unique words and a triphone based Context Dependent (CD) acoustic model for 1700 unique words have been built. In addition to the acoustic models a pronunciation dictionary with 44 base phones and trigram based statistical language model have also been built as integral components of the linguist. These recognizers give very good word accuracy for trained and test sentences read by trained and new speakers.
55 citations
TL;DR: A speech recognition system for Sichuan dialect is constructed by combining a hidden Markov model (HMM) and a deep long short-term memory (LSTM) network to overcome the problem that the DNN only captures the context of a fixed number of information items.
Abstract: In speech recognition research, because of the variety of languages, corresponding speech recognition systems need to be constructed for different languages. Especially in a dialect speech recognition system, there are many special words and oral language features. In addition, dialect speech data is very scarce. Therefore, constructing a dialect speech recognition system is difficult. This paper constructs a speech recognition system for Sichuan dialect by combining a hidden Markov model (HMM) and a deep long short-term memory (LSTM) network. Using the HMM-LSTM architecture, we created a Sichuan dialect dataset and implemented a speech recognition system for this dataset. Compared with the deep neural network (DNN), the LSTM network can overcome the problem that the DNN only captures the context of a fixed number of information items. Moreover, to identify polyphone and special pronunciation vocabularies in Sichuan dialect accurately, we collect all the characters in the dataset and their common phoneme sequences to form a lexicon. Finally, this system yields a 11.34% character error rate on the Sichuan dialect evaluation dataset. As far as we know, it is the best performance for this corpus at present.
24 citations