# Do We Get What We Need from Clinical Acoustic Voice Measurements?
## 2 Background: Current State of the Art and Challenges in Clinical Acoustic Measurements
### Relating Voice Quality Measures to Voice SPL, \(f_{0}\) and Token Type  
In the clinical application, we are faced with the dilemma of how to control for SPL and \(f_{0}\) without interfering with habitual voice production. In theory, there are different ways to address this. As discussed above, there are two focal points for technical and procedural questions.  
As shown by regression analysis, SPL-related effects on CPPS, jitter, shimmer, and HNR may be controlled for by using a correction factor/formula, or by calculating and reporting measurement results in reference to a standard voice intensity level (Figure 1). However, this approach does not take into account the associated natural increase in \(f_{0}\), which has its own effect on all of these measures. Another approach may be the application of voice range profiles (VRP) supplemented with acoustic quality data. These provide a voice quality measurement related to SPL and \(f_{0}\), thereby giving a voice function map related to the voice range profile [59; 60; 61]. In addition, difference maps have been shown to provide a quantitative analysis of voice changes while accounting for the individual's dependencies on SPL and \(f_{0}\) (Figures 2 and 3) [62]. However, patients will have to be verbally motivated and, probably, trained to provide comparable SPL and \(f_{0}\) ranges before and after treatment. This shows that technical solutions may not fully work without adaptation of voice tasks.  
Another possible method related to the recording procedure may be to ask patients to produce a specific predefined voice SPL, or to match their own SPL level from the first assessment. Studies in children and adults have shown that this is, in principle, feasible when providing visual feedback [41]. In addition, vowels could be examined at different loudness conditions, such as individually \"soft\", \"comfortable\", and \"loud\" levels, to evaluate changes in CPPS over a large SPL span within an individual.  
However, to date, these approaches have not been systematically explored and applied in voice disordered individuals. Moreover, the voice tasks themselves have been shown to influence natural voice behavior, which is presumed to contribute to voice disorders. Considering that natural speech is the gold standard for dysphonia in daily life, a variety of voice tasks should be combined, including very open tasks to induce much more prolonged natural speech, such as by asking for a standardized picture description. Combined with methodologically tightly controlled tokens, such as vowel phonation and speech of controlled phonetic content with control of SPL under visual feedback, this will cover both habitual and standardized phonation types. To date, there are not enough data available on how voice function changes with token and task (or verbal instruction) type, nor on which patients can perform which tasks. Since not all patients are able to perform every task, pathology-specific assessment protocols should be evaluated, such as proposed by [PERSON], et al. for hypokinetic or hyperkinetic dysarthria [9]. Furthermore, clinical acoustic analysis in comparatively large natural speech samples requires automatized tools with easy interfaces and affordable prices for widespread clinical implementation. This calls for far more research into technical solutions and suitable voice task types for the clinical application.