## 4 Results
### Multiple Regression Analysis  
To assess the efficacy of voice therapy based on changes in K-VRQOL scores, a multiple regression analysis was conducted. The independent variables included gender, smoking status, alcohol consumption, voice usage status, coffee consumption, presence of comorbidities, history of surgery, voice training experience, and acoustic parameters (Table 3). Gender was coded as 0 for males and 1 for females, and the remaining binary variables--such as smoking status, alcohol consumption, and others--were similarly coded (0 = no; 1 = yes). The dependent variable was defined as the difference in K-VRQOL scores before and after voice therapy, representing the degree of improvement in voice-related quality of life. Separate regression models were constructed for the total score, physical domain, and emotional domain to identify predictors associated with therapy-induced improvements. Table 3 presents the results of multiple regression analyses examining the factors influencing the changes in K-VRQOL scores before and after voice therapy. The dependent variables are the differences in the total, physical, and emotional scores, and various demographic, clinical, and acoustic parameters were entered as predictors. In terms of total K-VRQOL score changes, gender was a significant predictor (\(\beta\) = 0.347, \(p\) = 0.003), indicating that female participants exhibited greater improvements in overall voice-related quality of life following therapy. Additionally, the presence of comorbidities (\(\beta\) = 0.223, \(p\) = 0.025) was positively associated with a greater improvement, possibly suggesting higher baseline impairment leading to greater therapy responsiveness. Among the acoustic parameters, a significant negative association was observed for SFF (\(\beta\) = \(-\)0.260, \(p\) = 0.026), implying that a change in speaking fundamental frequency was linked to enhanced perceived voice-related quality of life. For the physical domain, gender again emerged as a strong predictor (\(\beta\) = 0.370, \(p\) < 0.001), along with comorbidity status (\(\beta\) = 0.265, \(p\) = 0.003). Notably, the acoustic parameters such as jitter (\(\beta\) = \(-\)0.214, \(p\) = 0.009), SFF (\(\beta\) = \(-\)0.293, \(p\) = 0.004), and MPT (\(\beta\) = 0.180, \(p\) = 0.041) were significantly associated with improvements in the physical aspects of voice-related quality of life. These results suggest  
Figure 2: Comparison of K-VRQOL scores pre- and post-voice therapy across physical (P), emotional (E), and total (T) domains. that improved frequency stability and increased phonation duration contribute positively to physical voice function recovery. In the emotional domain, smoking status was significantly associated with emotional score improvement (\(\beta\) = 0.238, \(p\) = 0.048), indicating that smokers may experience greater emotional benefits from voice therapy, although the underlying mechanism warrants further investigation. Additionally, SFF again showed a significant negative association (\(\beta\) = \(-\)0.248, \(p\) = 0.047), highlighting its consistent relevance across domains. Across all models, the VIF values were below 10, suggesting that multicollinearity among the predictors was not a concern. These findings underscore the importance of both demographic/clinical characteristics (e.g., gender, comorbidity, smoking) and acoustic features (especially SFF, jitter, and MPT) in predicting therapy-related improvements in voice-related quality of life.