# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 1 Introduction  
Laryngeal disorders are relatively common, affecting \(\sim\)5% according to [(1)] or 6.2% according to [(2)] of the general population, and are encountered in varying degrees of severity. Worldwide, larynx-related cancer causes about 200,000 annual deaths. This number keeps growing, while decreasing numbers of deaths related to many other types of cancer are observed. Therefore, efforts targeting preventive laryngeal health-care are required.  
Information obtained from both invasive and non-invasive measurements is used in laryngology for diagnostics and monitoring of treatment outcomes. Indirect laryngoscopy and video laryngostrooboscopy are probably the most informative modalities in diagnostics for laryngeal disorders [(3)]. However, sophisticated tools and an invasive course of actions required for obtaining such data impede wide scale monitoring of human larynx using such instrumentation.  
On the other hand, voice signals are obtained non-invasively and computer-based analysis of voice data is used increasingly in monitoring of treatment outcomes and screening for laryngeal disorders [(4; 5; 6; 7; 8; 9; 10; 11)]. Several measures computed from voice data are already widely used to quantify dysphonia changes and characterise outcomes of therapeutic and surgical treatment of laryngeal diseases [(8; 9; 10; 11)]. It was demonstrated that even a voice signal, transmitted through a telephone channel, contains much information for the task of laryngeal pathology detection [(12)].  
Questionnaire data, routinely collected by laryngologist, is another source of easily and non-invasively obtained information. As shown in [13], query data-based detection of disordered larynx consistently outperforms detection based on voice data, and fusion of these modalities improves the performance further. It was demonstrated that query data can provide useful information for pathology detection, which is not present in voice data [14]. In [13], large sets of voice and query features were used in a random forest [15] aiming to achieve low classification error in a two-class (healthy/pathological) classification task. By contrast, in this work, our focus is on small feature sets, transparent simple classification techniques and data exploration based on the suggested novel way to assess the similarity of observations.  
Various voice analysis tools, such as LingWaves (version 3.0, WEVOSYS medical technology GmbH, Baunach, Germany), Computerized Speech Lab (PENTAX Medical, Tokyo, Japan), and Dr. Speech (version 4, Tiger DRS, Seattle, Washington, USA), are usually used by laryngologists in clinical practice. Dr. Speech is very popular due to its low price and good documentation, but it is not really clear how to exploit full potential of analysis results available from the software [16]. To the best of our knowledge, there is no tool capable of exploiting both voice and query data in screening for laryngeal disorders. Some aspects of using query data in screening for laryngeal pathologies were explored from the medical viewpoint in [17; 18].  
The aim of this study is to create a user-friendly tool for identification of potentially pathological cases based on transparent analysis techniques and capable of exploiting both voice and query data. Currently, the tool is oriented towards experts working at departments of otolaryngology, but, in the near future, the developed tool could run on a smart phone, including voice recording and analysis, and be much more versatile.  
As a voice modality, sustained phonation of vowel /_a/_ has been used in this study, since sustained phonation is simple, lessens variance in sustained vowels and increases reliability of computed acoustic features [11; 19]. In addition, sustained phonation is rather unaffected by aspects related to different languages. Research concerning suitability of different vowels [20; 21] often concludes that vowel /_a/_ results in the lowest EER in laryngeal pathology detection.