Detecting Persian speaker-independent voice commands based on LSTM and ontology in communicating with the smart home appliances

Kalkhoran, Leila Safarpoor, Tabibian, Shima and Homayounvala, Elaheh (2022) Detecting Persian speaker-independent voice commands based on LSTM and ontology in communicating with the smart home appliances. Artificial Intelligence Review, 2022. pp. 1-29. ISSN 0269-2821

Text E.Homayounvala2022 Artifcial Intelligence Review Journal.pdf - Accepted Version Restricted to Repository staff only until 19 November 2023. Download (534kB) | Request a copy

Abstract / Description

Nowadays, various interfaces are used to control smart home appliances. The human and smart home appliances interaction may be based on input devices such as a mouse, keyboard, microphone, or webcam. The interaction between humans and machines can be established via speech using a microphone as one of the input modes. The Speech-based human and machine interaction is a more natural way of communication in comparison to other types of interfaces. Existing speech-based interfaces in the smart home domain suffer from some problems such as limiting the users to use a fixed set of pre-defined commands, not supporting indirect commands, requiring a large training set, or depending on some specific speakers. To solve these challenges, we proposed several approaches in this paper. We exploited ontology as a knowledge base to support indirect commands and remove user restrictions on expressing a specific set of commands. Moreover, Long Short-Term Memory (LSTM) has been exploited for detecting spoken commands more accurately. Additionally, due to the lack of Persian voice commands for interacting with smart home appliances, a dataset of speaker-independent Persian voice commands for communicating with TV, media player, and lighting system has been designed, recorded, and evaluated in this research. The experimental results show that the LSTM-based voice command detection system performed almost 1.5% and 13% more accurately than the Hidden Markov Model-based one, in scenarios ‘with’ and ‘without ontology’, respectively. Furthermore, using ontology in the LSTM-based method has improved the system performance by about 40%.

Item Type:	Article
Uncontrolled Keywords:	Voice commands detection, ontology, smart home appliances, Long Short-Term Memory, accessibility
Subjects:	000 Computer science, information & general works 600 Technology > 640 Home & family management
Department:	School of Computing and Digital Media
Depositing User:	Elaheh Homayounvala
Date Deposited:	03 Feb 2023 10:05
Last Modified:	03 Feb 2023 10:05
URI:	https://repository.londonmet.ac.uk/id/eprint/8045

Actions (login required)

View Item