# Speech Intelligibility Analysis and Approximation to Room Parameters through the Internet of Things
## 1 Introduction  
Since [PERSON] [1] published his research on reverberation in room acoustics, many researchers have devoted much effort to measuring acoustic behavior in indoor environments. The measurement procedure was standardized in ISO 3382 [2, 3, 4], and since then several revisions have been made. This standard makes some recommendations on different aspects related to the measurement procedure (e.g., exciting signal used, number of measurements, etc. ), but also leaves some non-mandatory aspects to allow innovation. It also opens the possibility of evaluating other parameters related to the amount of energy from the impulse response in every receiver location. Wireless Acoustic Sensor Networks (WASN) have allowed some technical automation of the measurement procedure following specific recommendations and following the ISO standard. Further research is needed to improve the measurement procedures by addressing different signal processing techniques [5, 6, 7, 8, 9] and the measurement protocols. Using WASN technology we gather the acoustic information of the whole room in one single shot from the sound source using a series of sensors distributed in the room. In recent years, WASN has been widely applied to different acoustic fields in indoor and outdoor environments. Most of these applications are aimed at locating one or several sources [10, 11], tracking [12] or identifying them [13], and measuring specific characteristics of the environment involved [14]. In [15], the authors use a WASN simulation to estimate the room acoustic performance, using localized and averaged Room Impulse Response(RIRs), and adapt the multipoint equalization of a sound system, to render the sound considering the inversion of the room acoustic model, and applied as a pre-filter to the \"dry\" signal before playback. Most parameters used for acoustic analysis of rooms such as Reverberation Time 30 (TR30), TR60 and Clarity 80 and most of the parameters used for speech transmission analysis such as Speech Transmission Index (STI), Clarity 50 and Speech Intelligibility Index (SII) [16] make us think of traditional spaces such as theaters, cinemas, music schools or music production rooms. However, it is interesting and very relevant to make use of such parameters in rooms dedicated to other purposes where the transmission of the word is vital, such as teaching spaces or those dedicated to medical care, to assess their behavior and suitability [17; 18; 19] of speech transmission. The afore mentioned parameters are calculated based on the impulse response of the room with the only exception of the SII [20], which is extracted from a speech signal recorded in the position of the room where we wish to analyze intelligibility. The calculation of parameters based on impulse response is much more complex than SII, so it is not possible to do it in a simple, fast or effective way in a node of our WASN, as it happens for example with the parameters of psychoacoustic disturbance [21]. In fact, we proposed to implement a convolutional neuronal network (CNN) that would allow us to successfully predict them, just as we did with the parameters of psychoacoustic disturbance [21]. Psychoacoustic annoyance quantifies how disturbing different sounds can be to humans. For this purpose, different psychoacoustic parameters (Loudness, Sharpness, Roughness and Fluctuation Strength) are calculated and used to calculate an annoyance marker: Psychoacoustic Annoyance. In this previous work, we used a CNN to perform the prediction of these parameters in a fast and accurate way and to be able to perform the calculations in the node of a WASN itself. The implementation of SII from a speech signal recorded in any room location we wish to analyze, makes it much easier the necessary signal processing and therefore the calculation capacity of the devices used.