# Real-Time Communication Aid System for Korean Dysarthrie Speech
## 1 Introduction  
Dysarthria is a type of speech disorder caused by abnormalities in the motor neurons, which impair the control of the articulatory organs necessary for speech production [1]. It is classified as a type of language disorder along with language comprehension and expression disorders, as well as voice disorders. Although there is no universally accepted objective classification system for dysarthria, it is estimated to account for approximately 30% of all speech disorders [2, 3]. There are many factors that contribute to the development of dysarthria, including brain function problems, speech problems, hearing problems, and laryngeal problems [4], and many people have difficulty communicating socially due to dysarthria [4, 5]. Unlike aphasia, which is caused by damage to the language centers of the brain and results in an inability to speak fluently, dysarthria occurs when motor control of the speech organs is impaired due to neurological problems, leading to difficulties in articulation and phonation while retaining the ability to generate speech. In contrast to aphasia, which prevents speech production entirely, dysarthria is caused by neurological disorders and canstill produce speech. However, the pathological intelligibility of the speech is significantly reduced [6,7]. Therefore, effective treatment for dysarthria often involves the use of various communication aids along with speech therapy to improve the clarity of speech. Studies to develop various assistive tools for treating dysarthria have been continuously conducted over the years [8, 9, 10]. Among the various assistive tools for treating dysarthria, one of the most prominent assistive tools is Augmentative and Alternative Communication (AAC). AAC is an augmentative and alternative form of communication that helps people with speech or vocalization difficulties to communicate effectively. It can be implemented through diverse means such as pictures, symbols, boards, or electronic devices, and is widely used for people with different speech disorders, including cerebral palsy, developmental disabilities, and dysarthria [8]. In particular, Voice Output Communication Aids (VOCAs), which convey a user's intended message through speech output, is based on inputs like text entry or the selection of pictures or symbols. VOCAs have been shown to be effective in improving communication for individuals with speech impairments [9]. A notable example of VOCAs research is the Voice-Input Voice-Output Communication Aid (VIVOCA), which takes simple speech inputs from individuals with dysarthria in specific situations, recombines them, and produces speech output through voice synthesis [10]. In addition to AAC, a prominent method for aiding communication in individuals with dysarthria is Automatic Speech Recognition (ASR) technology. If ASR can accurately recognize speech from individuals with dysarthria, it could be used in various ways to support their communication needs [11]. Research trends in ASR technology for dysarthria suggest that ASR studies based on early machine learning methods began in the mid-1990s [12, 13, 14]. From the 2010s onward, research focused on developing ASR models using deep learning technologies and exploring ways to augment the limited speech data available for dysarthria [15,16]. Subsequently, to enhance model performance, studies on Audio-Visual Speech Recognition (AVSR) were introduced, which involved learning both the speech sounds of dysarthria and the speaker's lip movements during speech [17, 18, 19]. Furthermore, Speak Vision, which interprets speech data visually, has been introduced, and ongoing research continues to improve ASR for individuals with dysarthria [20,21]. Recently, ASR performance for elderly individuals and those with dysarthria have been significantly advanced [22, 23, 24].