Commanding mobile robot movement based on natural language processing with RNN encoder­decoder

Kahuttanaseth, Wittawin; Dressler, Alexander; Netramai, Chayakorn

doi:10.1109/icbir.2018.8391185

Cited by 8 publications

(7 citation statements)

References 2 publications

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“…To overcome this limitation, Long-Term Short-Term Memory (LSTM) have been proposed in [33][34][35] as a particular type of RNN. These models explicitly capture recursive temporal correlations and they have already proven their effectiveness in various fields, such as speech recognition [36], natural language processing [37] and image completion [38]. LSTMs have recently been used in medical imaging such in [39] where the authors propose a method with multi-modality and adjacency constraints for the segmentation of the cerebral image.…”

Section: Related Workmentioning

confidence: 99%

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

Fakhfakh

Bouaziz

Gargouri

et al. 2020

Preprint

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Humanity is facing nowadays a dramatic pandemic episode with the Coronavirus propagation over all continents. The Covid-19 disease is still not well characterized, and many research teams all over the world are working on either therapeutic or vaccination issues. Massive testing is one of the main recommendations. In addition to laboratory tests, imagerybased tools are being widely investigated. Artificial intelligence is therefore contributing to the efforts made to face this pandemic phase. Regarding patients in hospitals, it is important to monitor the evolution of lung pathologies due to the virus. A prognosis is therefore of great interest for doctors to adapt their care strategy. In this paper, we propose a method for Covid-19 prognosis based on deep learning architectures. The proposed method is based on the combination of a convolutional and recurrent neural networks to classify multi-temporal chest X-ray images and predict the evolution of the observed lung pathology. When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%.All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Section: Related Workmentioning

confidence: 99%

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

Fakhfakh

Bouaziz

Gargouri

et al. 2020

Preprint

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“…Speech commands were input to a microphone and converted into digital potential signals by an analog-to-digital converter (ADC). After the features had been obtained, the LSTM (10) in a recurrent neural network (RNN) (11) was used to perform speech recognition. Speech recognition was accomplished using voice signal preprocessing, speech eigenvalue extraction, and the RNN.…”

Section: Speech Recognitionmentioning

confidence: 99%

Dual-input Control Interface for Deep Neural Network Based on Image/Speech Recognition

Pai¹,

Chen²,

Hung³

et al. 2019

Sensors and Materials

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The objective of this study was to design a control interface for dual-input video/audio recognition consisting of two input interface systems, hand posture and speech recognition, with the use of specific hand postures or voice commands for control without the need for wearable devices. Original video camera images were preprocessed for hand posture recognition, and the face in the image was used as the reference point and identified using the Adaboost classifier. An image of a specific size was selected as the recognition input image to increase the recognition speed. A neural network comprising convolutional, activation, max pooling, and fully connected layers was used to classify and recognize hand posture images as well as speech. Long short-term memory (LSTM) in a recurrent neural network (RNN) was used to achieve speech recognition. Speech features were extracted by preprocessing, and Melfrequency cepstral coefficients (MFCCs) and a fast Fourier transform (FFT) were then used to convert the signals from the time domain to the frequency domain. The frequency domain signals subsequently underwent a discrete cosine transform through triangular bandpass filters to derive MFCCs as the speech eigenvalue input. The speech feature parameters were then input to the LSTM neural network to make predictions and achieve speech recognition. Experimental results showed the image/speech dual-input control interface had good sound recognition capability, supporting the findings of this study.

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“…Nowadays, the interaction between humans and robots in daily life has become a common activity, mainly to accomplish useful tasks for humans [1]. An important challenge is enabling robots to execute instructions conveyed in natural language (e.g., commands).…”

Section: Introductionmentioning

confidence: 99%

Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

Hernández,

Ortega-Mendoza,

Villatoro-Tello

et al. 2024

Mathematics

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Human–robot interaction is becoming increasingly common to perform useful tasks in everyday life. From the human–machine communication perspective, achieving effective interaction in natural language is one challenge. To address it, natural language processing strategies have recently been used, commonly following a supervised machine learning framework. In this context, most approaches rely on the use of linguistic resources (e.g., taggers or embeddings), including training corpora. Unfortunately, such resources are scarce for some languages in specific domains, increasing the complexity of solution approaches. Motivated by these challenges, this paper explores deep learning methods for understanding natural language commands emitted to service robots that guide their movements in low-resource scenarios, defined by the use of Spanish and Nahuatl languages, for which linguistic resources are scarcely unavailable for this specific task. Particularly, we applied natural language understanding (NLU) techniques using deep neural networks and transformers-based models. As part of the research methodology, we introduced a labeled dataset of movement commands in the mentioned languages. The results show that models based on transformers work well to recognize commands (intent classification task) and their parameters (e.g., quantities and movement units) in Spanish, achieving a performance of 98.70% (accuracy) and 96.96% (F1) for the intent classification and slot-filling tasks, respectively). In Nahuatl, the best performance obtained was 93.5% (accuracy) and 88.57% (F1) in these tasks, respectively. In general, this study shows that robot movements can be guided in natural language through machine learning models using neural models and cross-lingual transfer strategies, even in low-resource scenarios.

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Commanding mobile robot movement based on natural language processing with RNN encoderdecoder

Cited by 8 publications

References 2 publications

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

Dual-input Control Interface for Deep Neural Network Based on Image/Speech Recognition

Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

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Commanding mobile robot movement based on natural language processing with RNN encoder­decoder

Cited by 8 publications

References 2 publications

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

Dual-input Control Interface for Deep Neural Network Based on Image/Speech Recognition

Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

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Commanding mobile robot movement based on natural language processing with RNN encoderdecoder