A Voice Command Detection system for controlling Movement of SCOUT Robot

Azargoshasb, Samaneh; Korayem, Moharam Habibnejad; Tabibian, Sh.

doi:10.1109/icrom.2018.8657523

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…We will introduce them, in the following paragraph. [24,25] . MFCCs are based on the human auditory and perceptual system and have also shown acceptable performance in various studies [25].…”

Section: -2-the Proposed Voice Command Detection Systemmentioning

confidence: 99%

Multimodal user interaction with in-car equipment in real conditions based on touch and speech modes in the Persian language

Nazari

Tabibian

Homayounvala

2022

Multimed Tools Appl

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Nowadays, communication with in-car equipment is performed via a large number of buttons or a touch screen. This increases the need for driver's visual attention and leads to reduce the concentration of drivers while driving. Speech-based interaction has been introduced in recent years as a way to reduce driver distractions. This input mode faces several technical challenges such as the need to memorize voice commands and the difficulties of canceling them. This paper focuses on presenting a multimodal user interface design based on touch and speech modes, for controlling five in-car devices (radio, CD player or music player, fan, heater, and driver-side window). The research is designed to collect a dataset of in-car voice commands in the Persian language in real conditions (in a real car and in the presence of background noises) to firstly create a dataset of Persian voice commands (due to lack of research in this area in Persian speaking countries) and secondly intending to solve the mentioned challenges. To evaluate the proposed user interface, 15 participants performed ten different tasks based on the speech and touch modes, with and without driving simulation. The evaluation results indicated that the speech input mode with and without driving simulation has had in average smaller number of clicks for performing tasks (0.2 and 0.6), smaller task completion time (7.37 and 3.3 seconds), smaller time intervals between clicks (8.2 and 5 seconds) and smaller driver's distraction rate (25.08%) in comparison to the touch input mode, respectively. Moreover, using two different input modes in designing the in-vehicle user interface leads to increased accessibility.

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Section: -2-the Proposed Voice Command Detection Systemmentioning

confidence: 99%

Multimodal user interaction with in-car equipment in real conditions based on touch and speech modes in the Persian language

Nazari

Tabibian

Homayounvala

2022

Multimed Tools Appl

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“…They assess the effectiveness of various strategies in terms of processing speed and word error rate (WER) [19]. For directing the movement of a scout robot, a voice command detecting system is proposed in [20]. For specialized tasks including movement control, obstacle avoidance, and picture capturing, the system was created to recognize a set of predefined spoken commands [20].…”

Section: Introductionmentioning

confidence: 99%

“…For directing the movement of a scout robot, a voice command detecting system is proposed in [20]. For specialized tasks including movement control, obstacle avoidance, and picture capturing, the system was created to recognize a set of predefined spoken commands [20]. The usage of Transformer and CNN based models for ASR is discussed in [21].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing crowd surveillance through voice-driven face recognition empowering rapid identification: towards development of sustainable smart cities

Bhat,

Paul,

Sahu

et al. 2024

Eng. Res. Express

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Recent global efforts to create sustainable smart cities have significantly transformed society and improved the lives of people. Nowadays, crowd surveillance (CS) has become essential in sustainable smart cities and society to protect public safety and security. In this regard, the face-based human detection system has received considerable attention because it is recognized as an emerging method in crowd surveillance applications. Thus, in this work, a new method for real-time identification of people for a crowd surveillance system (CSS) that uses facial and speech recognition technology has been introduced. In traditional CS systems, human operators are frequently used by crowd surveillance systems to watch and evaluate video feeds. Human error and operator weariness may result in lost opportunities or slow replies, which reduce the system's efficacy. Certain procedures, including the initial identification and monitoring of people in video feeds, can be automated using a voice-activated system. To address the issues with the present CSS, a new framework Voice-Activated Face Recognition (VAFR) is proposed in this work. The proposed framework combines the speech and face recognition models for crowd surveillance. Experimental and simulation studies have been performed to analyze the performance of the proposed VAFR framework. The proposed framework uses the Viola-Jones algorithm for face identification and the Conformer architecture for speech analysis, reaching a noteworthy 99.7% accuracy rate in live video feeds. In addition, the ethical and safety aspect of the proposed VAFR system is presented.

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“…On the other hand, the implementation of speech recognition also supports robot control [7]. Azargoshasb et al use voice command recognition to control the robot [8]. The commands given are "go", "stop", "left", "right", "forward", "backward", etc.…”

Section: Introductionmentioning

confidence: 99%

Implementation of MFCC and SVM for Voice Command Recognition as Control on Mobile Robot

Rendyansyah¹

2022

JurnalEcotipe

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The mobile robot is a system that can move according to function and task. An example is an industrial robot taking objects using a remote control system. Robots controlled using a manual remote system are generally carried out on mobile robots. Many researchers have developed manual control methods, such as image or sound-based robot control. In this study, the mobile robot was applied in an unobstructed room and controlled using voice commands. The methods used are Mel-Frequency Cepstral Coefficients (MFCC) and Support Vector Machine (SVM). MFCC is a characteristic identification of voice command patterns such as “forward”, “backward”, “left”, “right”, and “stop.” SVM is used to recognize voice command patterns based on the value of the MFCC for each pattern. The experiment has been carried out 50 times with a success rate of 96%. Overall the robot can be controlled by voice commands with good movement.

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A Voice Command Detection system for controlling Movement of SCOUT Robot

Cited by 9 publications

References 13 publications

Multimodal user interaction with in-car equipment in real conditions based on touch and speech modes in the Persian language

Multimodal user interaction with in-car equipment in real conditions based on touch and speech modes in the Persian language

Revolutionizing crowd surveillance through voice-driven face recognition empowering rapid identification: towards development of sustainable smart cities

Implementation of MFCC and SVM for Voice Command Recognition as Control on Mobile Robot

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