UNNIGSA: A Unified Neural Network Approach for Enhanced Stutter Detection and Gait Recognition Analysis

Stuttering, also known as stammering, is a speech disorder characterized by involuntary disruptions or disfluencies in a person's flow of speech. These disfluencies may include repetitions of sounds, syllables, or words; prolongations of sounds; and interruptions in speech known as blocks. This paper introduces Unified Neural Network for Integrated Gait and Speech Analysis (UNNIGSA), methodology that synergizes stutter detection (SD) and gait recognition through a unified neural network architecture. UNNIGSA is engineered to address two distinct yet interrelated challenges: the accurate detection of stuttering for enhanced beneficial interventions and the precise identification of individuals based on gait analysis. The system integrates a global attention mechanism to meticulously highlight salient features within speech patterns, thereby improving the accuracy of stutter classification and offering a potential leap forward in speech therapy practices. Additionally, UNNIGSA incorporates novel data processing techniques to manage the class imbalance prevalent in stuttering speech datasets, resulting in significantly enhanced performance over existing models. The methodology also extends the functionality of automatic speech recognition (ASR) systems, fostering greater inclusivity for individuals with speech disorders and enabling their more seamless interaction with virtual assistant technologies. Overall, UNNIGSA sets a new standard in the domains of speech disorder treatment and biometric identification, offering innovative solutions to long-standing challenges and paving the way for more inclusive and secure applications.