# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 1 Introduction  
A series of voice analysis tools includes many components to evaluate speech functions and voice quality [1; 2]. The quantitative evaluation of voice quality using acoustics is advocated [3], and two metrics in this sector are the Acoustic Voice Quality Index (AVQI) and the Acoustic Breathiness Index (ABI) [4]. In the assessment of prolonged phonation and continual speech, multiparametric indexes enable a more robust acoustic analysis of voice quality by taking into account more than one acoustic parameter. However, if continuous speech is included, linguistic discrepancies must be handled [5]. The goal of [PERSON]'s research [6] was to evaluate the acoustic metrics of fundamental frequency, standard deviation, jitter, shimmer, noise-to-harmonic ratio, smoothed cepstral peak prominence (CPPS), and AVQI evaluated invariably by VoiceEvalU8 or manually by two researchers. They discovered that the measurements were of good to exceptional reliability. Furthermore, the human voice is dynamic and evolves over time. The influences of age and sex on acoustic measurements of voice quality are well-known, yet AVQI remains independentof gender [7]. Compared to adults, the AVQI achieved by the pediatric and older adult groups was shown to be substantially higher. AVQI also exhibited significant age effects. Adults' AVQI levels were shown to be more constant than those of children and the elderly. The AVQI scores of elderly adults and children did not differ substantially [8]. [PERSON] study examined and contrasted the acoustic short-term effects of pitch elevation training (PET) In transgender women, also focusing on articulation-resonance training (ART), and a combination of both programs. Xirs discovered that fundamental frequencies rose after both PET and ART programs, with a greater increase after PET, but that intensity and voice quality measures did not become altered [9]. Artificial intelligence (AI) approaches can be used to analyze data, reduce computing time, and optimize decision-making processes and forecasts in a variety of industries, including healthcare [10]. Advances in mobile device technology provide new possibilities for remote speech monitoring at home and in clinical settings. A simple mobile device is transformed into a screening tool for the early detection, monitoring, and treatment of voice abnormalities. There is, nevertheless, a necessity to demonstrate equivalence between characteristics generated from mobile device signals and gold-standard microphone preamplifiers. Acoustic speech qualities from Android phone, tablets, and microphone preamplifier records were compared in the study of [11]. Compared to conventional PC-based voice treatment, smartphone voice therapy is less expensive and more flexible for patients and doctors. According to [12], voice quality and patient satisfaction increased in both therapies compared to before therapy, showing recovery. Others discovered that AVQI measures obtained from smartphone microphone voice recordings with experimentally added ambient noise disclosed an amicable settlement with the results of oral microphone recordings, implying that smartphone microphone recordings conducted, even with the existence of acceptable ambient noise, are suitable for estimating AVQI [13]. [PERSON] wanted to see how standardized mobile phone recordings transferred across a telecom channel affected acoustic indicators of speech quality and how voice specialists perceived it in normophonic speakers [14]. The results reveal that sending a speech signal across a telephone line causes filtering and noise effects, limiting the use of popular acoustic sound quality metrics and indexes. The recording type has a considerable influence on both the AVQI and the ABI. Pitch perturbation (local jitter and periodic standard deviation) and the harmonics-to-noise ratio from Dejonckere and Lebacq appear to be the most reliable acoustic metrics.