# Do We Get What We Need from Clinical Acoustic Voice Measurements?
## 2 Background: Current State of the Art and Challenges in Clinical Acoustic Measurements
### Dysphonia Limits the Objective Measurement of Vocal Quality  
Dysphonic voice signals are characterized by an increase in irregularity related to fundamental frequency and voice intensity, and reduced organization or energy in the overtone spectrum. Thus, so-called traditional time-based vocal quality measures, including jitter (\(f_{0}\) variation) and shimmer (SPL variation), will have increased values with signal irregularity. Spectral measures such as Harmonics-to-Noise ratio (HNR) and Cepstral Peak Prominence (CPP) will decrease due to irregularity, as will indices incorporating these measures [4; 5; 6]. Single and combined acoustic voice quality measures have been related to breathiness, roughness, and overall dysphonia in a comparatively large body of literature [5; 21; 22; 23]. Moreover, the discrimination of dysphonia may be possible with an Area Under the Curve (AUC) of up to 91% for CPPS as single measure [24]. Combined indices, including variants of CPP, HNR, and shimmer or jitter, showed an AUC of up to 94% for overall dysphonia and breathiness [21; 23]. Based on the current literature, we can conclude that there is clearly a relation between perceptual dysphonia or voice disorder and several inferential acoustic measures. However, in a clinical population there are several pragmatic problems to address. Measures such as jitter, shimmer, and HNR rely on correct recognition of \(f_{0}\) and SPL and, in the case of HNR, also on successful spectrum analysis. Since acoustic signals may simply be too irregular for these analysis steps, it has been recommended that different analysis strategies be applied based on voice signal regularity. According to [PERSON], nearly periodic _Type 1_ voices were classified as highly suitable for perturbation (such as jitter and shimmer) analysis. _Type 2_ voices with strong modulations or subharmonics approaching/\(f_{0}\) in energy, as well as irregular or aperiodic _Type 3_ signals, were recommended for spectrograph and perceptual analysis methods [6]. As an addition to this model, _Type 4_ voices have been described as Type 3 signals that are stochastic in nature and, therefore, suitable for nonlinear analysis [25]. Thus, in more irregular Type 3 or 4 signals, results of \(f_{0}\) and SPL-based measures may be erroneously high [25]. In principle, similar measurement problems apply to indices incorporating one of these parameters, such as the DSI, Cepstral Spectral Index of Dysphonia (CSID), or Acoustic Voice Quality Index (AVQI) [7; 26]. Since a typical clinical population incorporates approximately 20% of patients with so-called Type 3 or 4 signals, this will result in potentially distorted results in analyses of unselected voice-disordered populations [27]. Other methods, now increasingly based on machine learning, have been applied to several databases in efforts to improve the automatic classification of voice disorders. The results are promising, but more studies in the field are still needed [28]. As a consequence, we cannot simply assume that every acoustic measure can be reliably applied to every voice patient. To address this issue, CPP or CPPS based on spectral analysis were recommended for clinical voice assessments in 2019 [5]. However, from an international viewpoint, there is still a broad application of parameters such as jitter and shimmer, which are not applicable, per se, in severely dysphonic voices. As an illustration, in June 2022, we performed a short search in Pubmed using the keywords \"CPP dysphonia\" and \"jitter dysphonia\" for the timespan of 2019 to 2022. This resulted in 30 findings for \"CPP\", while \"jitter\" resulted in 88 entries in the same period. Moreover, combined indices incorporating jitter or shimmer variants have been increasingly applied to dysphonic voices [23; 28]. Therefore, recommendations for measurements of dysphonic voices should include a clear characterization of suitable voice signal quality for different types of parameters, including combined indices.