The continuous rising of the elderly/disabled population has created a requirement for assistive robotics devices to counter the lack of trustworthy servants. Intelligent Wheelchairs are developed for that particular purpose. Intelligent Wheelchairs differ depending on the interactive modality and most commonly found modalities are speech-controlled. Since these are assistive devices that need to act as human companions, it is necessary to have a dialogue between the device and the user. Even though the wheelchair is fully automated, the user should have control over it at some point. However, this exchange of control should be intelligent and transitions need to be executed in order to safeguard the user. Therefore the purpose of this paper is to propose an intelligent system that would navigate an intelligent voice-controlled wheelchair facilitating the intelligent exchange of control between the user and the wheelchair. This control is not simultaneous and one can override the other only when navigation could lead to collisions. In the proposed method, users can control the wheelchair using fixed vocal commands, and execution of those commands will be performed using the spatial and control parameters. Control of the wheelchair will be exchanged between the user and the wheelchair itself considering specific parameters such as obstacle distance, collision time, the velocity of the wheelchair among others. User control mode has 5 definite vocal commands with classifiers to identify any navigation command into the command model and considers uncertain terms such as ‘little’ and ‘hard’ for ‘Turn’ commands. Command classification had produced a Cohen’s Kappa value of 0.9462 and the classifier for the uncertain terms had produced a Cohen’s Kappa value of 0.7325. Both were acceptable values for those particular classifications. As per the experiment results, the proposed system reduced the vocal command frequency and risk of collisions through proper control of the velocity levels and intelligent exchange of control at given locations.

Accurate pronunciation is essential for successful communication in a second language (L2) as it significantly influences communicative effectiveness and perceived fluency. Mispronunciations frequently arise due to the influence of the learner’s first language (L1), posing barriers to effective spoken communication. Therefore, pronunciation error detection (PED) has emerged as a critical research area within the domains of Computer-Assisted Language Learning (CALL) and Computer-Assisted Pronunciation Training (CAPT). Although numerous PED systems have been developed over recent decades, existing survey papers have mainly emphasized comparisons of modeling methodologies or learning paradigms, often neglecting the critical role of feature representation. To address this research gap, this survey introduces a novel, feature-based taxonomy for categorizing PED methodologies into four primary groups: Acoustic-based, Acoustic-Phonetic, Linguistic-based, and Hybrid approaches. Each category is systematically reviewed, summarizing over two decades of research work with respect to feature extraction techniques, modeling approaches, evaluation metrics, and the nature and quality of instructional feedback provided to learners. A detailed comparative analysis highlights significant trade-offs among these categories in terms of detection accuracy, interpretability, resource demands, and applicability in real-time or low-resource contexts. Furthermore, this survey discusses recent and emerging trends in PED research, including self-supervised learning frameworks, multimodal feature fusion, and integrating phonological knowledge with modern deep learning architectures. By synthesizing existing knowledge and identifying gaps in current methodologies, this paper aims to provide clear insights and directions for future advancements in PED systems.

Natural human–human communication consists of multiple modalities interacting together. When an intelligent robot or wheelchair is being developed, it is important to consider this aspect. One of the most common modality pairs in multimodal human–human communication is speech–hand gesture interaction. However, not all the hand gestures that can be identified in this type of interaction are useful. Some hand movements can be misinterpreted as useful hand gestures or intentional hand gestures. Failing to filter out these unintentional gestures could lead to severe faulty identifications of important hand gestures. When speech–hand gesture multimodal systems are designed for disabled/elderly users, the above-mentioned issue could result in grave consequences in terms of safety. Gesture identification systems developed for speech–hand gesture systems commonly use hand features and other gesture parameters. Hence, similar gesture features could result in the misidentification of an unintentional gesture as a known gesture. Therefore, in this paper, we have proposed an intelligent system to filter out these unnecessary gestures or unintentional gestures before the gesture identification process in multimodal navigational commands. Timeline parameters such as time lag, gesture range, gesture speed, etc., are used in this filtering system. They are calculated by comparing the vocal command timeline and gesture timeline. For the filtering algorithm, a combination of the Locally Weighted Naive Bayes (LWNB) and K-Nearest Neighbor Distance Weighting (KNNDW) classifiers is proposed. The filtering system performed with an overall accuracy of 94%, sensitivity of 97%, and specificity of 90%, and it had a Cohen’s Kappa value of 88%.