Objective speech intelligibility measurement techniques like AI (Articulation Index) and AI based STI (Speech Transmission Index) fail to assess speech intelligibility in modern telecommunication networks that use several non-linear processing for enhancing speech. Moreover, these techniques do not allow prediction of single individual CVC (Consonant Vowel Consonant) word intelligibility scores. ITU-T P.863 standard [1], which was developed for assessing speech quality, is used as a starting point to develop a simple new model for predicting subjective speech intelligibility of individual CVC words. Subjective intelligibility measurements were carried out for a large set of speech degradations. The subjective test uses single CVC word presentations in an eight alternative closed response set experiment. Subjects assess individual degraded CVC words and an average of correct recognition is used as the intelligibility score for a particular CVC word. The first subjective database uses CVC words that have variations in the first consonant i.e. /C/ous (represented as "kæʊs" using International Phonetic Association phonetic alphabets). This database is used for developing the objective model, while a new database based on VC words (Vowel Consonant) that uses variations in the second consonant (a/C/ e.g. aH, aL) is used for validating the model. ITU-T P.863 shows very poor results with a correlation of 0.30 for the first subjective database. A first extension to make P.863 suited for intelligibility prediction is done by restructuring speech material to meet the temporal structure requirements (speech+silence+speech) set for standard P.863 measurements. The restructuring is done by concatenating every original and degraded CVC word with itself. There is no significant improvement in correlation (0.34) when using P.863 on the restructured first subjective database (speech material meets temporal requirements).  In this thesis a simple model based on P.863 is developed for assessing intelligibility of individual CVC words. The model uses a linear combination of a simple time clipping indicator (missing speech parts) and a “Good frame count” indicator which is based on the local perceptual (frame by frame) signal to noise ratio. Using this model on the restructured first database, a reasonably good correlation of 0.81 is seen between subjective scores and the model output values. For the validation database, a correlation of around 0.76 is obtained. Further validation on an existing database at TNO, which uses time clipping degradation only, shows an excellent correlation of 0.98. Although a reasonably good correlation is seen on the first database and the validation database, it is too low for reliable measurements. Further validation and development is required, nevertheless the results show that a perception-based technique that uses internal representations of signals can be used for predicting subjective intelligibility scores of individual CVC words.

This thesis is focused on objective analysis of data obtained during quality of service measurements provided by the company O2 Czech Republic Inc. Purpose of this study is mapping of existing methods, resources and ways of measurement. The result of practical part is design of new methodology of measurement for the department RAN Management, which is responsible for measurement of quality of services.

This master’s thesis deals with application of the ITU-T G.107 E-model for objective non-intrusive voice transmission quality measurements in LTE and Wi-Fi networks. The first part presents techniques used for voice quality measurements, particularly algorithm POLQA, analysis of the E-model and overview of the VoLTE and VoWiFi technologies. The main part of this paper consists of design of the R-factor calculation formula using parameters measured by Android OS powered devices. The algorithm design is based on extensive VoLTE measurements and its voice quality prediction successfulness is evaluated by comparison with POLQA measurements. The paper also presents implementation possibilities of the proposed algorithm on devices with Android OS.