The paper presents an analysis of the voicing of the phoneme /v/ in modern spoken Macedonian. The phoneme /v/ in the standard Macedonian language is classifi ed as a fricative, but some of its characteristics separate it from the other phonemes in this group. This is due to the fact that this phoneme was once a sonorant. In a part of the Macedonian dialects this phoneme is pronounced with marked voicing to this day. This phenomenon is then refl ected in the pronunciation of standard Macedonian. Our analysis is based on a selected corpus of examples that have been spoken by speakers from various dialect origins, in order to assess the any differences in pronouncing of the phoneme /v/ when placed in different phoneme contexts in the word.

Choral singers are among intensive voice users whose excessive vocal effort puts them at risk of developing voice disorders. The aim of the work was to assess voice quality for choral singers in the choir at the Polish-Japanese Academy of Information Technology. This evaluation was carried out using the acoustic parameters from the COVAREP (A Collaborative Voice Analysis Repository For Speech Technologies) repository. A prototype of a mobile application was also prepared to allow the calculation of these parameters.

The study group comprised 6 male and 19 female choir singers. The control group consisted of healthy non-singing individuals, 50 men and 39 women. Auditory perceptual assessment (using the RBH scale) as well as acoustic analysis were used to test the voice quality of all the participants. The voice quality of the female choir singers proved to be normal in comparison with the control group.

The male choir singers were found to have tense voice in comparison with the controls. The parameters which proved most effective for voice evaluation were Peak Slope and Normalized Amplitude Quotient.

Voice production — emission, raised interest of humans from almost the beginning of the humanity. First written information dates back to the Egyptian times 2500–3000 BC. Practically from early Greek period until XIX century studies of the larynx and the speech apparatus brought new and new facts, both regarding the structures, physiology and clinics. Such ancient researchers as Galen, Morgagni, Eustachii, Casserius created milestones for modern laryngology. Authors hoped to present some facts on the anatomical researches in the field of organs responsible for voice production from historical perspective.

Hereby there is given the speaker identification basic system. There is discussed application and usage of the voice interfaces, in particular, speaker voice identification upon robot and human being communication. There is given description of the information system for speaker automatic identification according to the voice to apply to robotic-verbal systems. There is carried out review of algorithms and computer-aided learning libraries and selected the most appropriate, according to the necessary criteria, ALGLIB. There is conducted the research of identification model operation performance assessment at different set of the fundamental voice tone. As the criterion of accuracy there has been used the percentage of improperly classified cases of a speaker identification.

The paper investigates the interdependence between the perceptual identification of the vocalic quality of six isolated Polish vowels traditionally defined by the spectral envelope and the fundamental frequency F0. The stimuli used in the listening experiments were natural female and male voices, which were modified by changing the F0 values in the ±1 octave range. The results were then compared with the outcome of the experiments on fully synthetic voices. Despite the differences in the generation of the investigated stimuli and their technical quality, consistent results were obtained. They confirmed the findings that in the perceptual identification of vowels of key importance is not only the position of the formants on the F1 × F2 plane but also their relationship to F0, the connection between the formants and the harmonics and other factors. The paper presents, in quantitative terms, all possible kinds of perceptual shifts of Polish vowels from one phonetic category to another in the function of voice pitch. An additional perceptual experiment was also conducted to check a broader range of F0 changes and their impact on the identification of vowels in CVC (consonant, vowel, consonant) structures. A mismatch between the formants and the glottal tone value can lead to a change in phonetic category.

Dialogue in the Classroom: Teaching Strategies and Their Reception by Students – The paper aims to explore Student Voice research within the academic context in terms of theoretical assumptions and a practical approach to its application in the classroom. In the first part, we focus on three main themes which build the explanatory framework: (1) Italian language teaching at Polish universities, (2) the current teaching methodology implemented in the classroom, and (3) Student Voice as a tool to better plan teaching activities. In the second part, we present the findings of a survey conducted among students learning Italian at the Faculty of Applied Linguistics, and we analyze their value for the teaching and learning process.

Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.

The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.

The goal of this article is to present and compare recent approaches which use speech and voice analysis as biomarkers for screening tests and monitoring of some diseases. The article takes into account metabolic, respiratory, cardiovascular, endocrine, and nervous system disorders. A selection of articles was performed to identify studies that assess voice features quantitatively in selected disorders by acoustic and linguistic voice analysis. Information was extracted from each paper in order to compare various aspects of datasets, speech parameters, methods of applied analysis and obtained results. 110 research papers were reviewed and 47 databases were summarized. Speech analysis is a promising method for early diagnosis of certain disorders. Advanced computer voice analysis with machine learning algorithms combined with the widespread availability of smartphones allows diagnostic analysis to be conducted during the patient’s visit to the doctor or at the patient’s home during a telephone conversation. Speech analysis is a simple, low-cost, non-invasive and easy-toprovide method of medical diagnosis. These are remarkable advantages, but there are also disadvantages. The effectiveness of disease diagnoses varies from 65% up to 99%. For that reason it should be treated as a medical screening test and should be an indication of the need for classic medical tests.

This work is complementary with Bogusław Wolniewicz’s text Elzenberg about Milosz. The circumstances surrounding the discovery of Czesław Milosz’s article Duty and Henryk Elzenberg’s polemic are portrayed here. Moreover, in the second part we have attempted to evaluate Joseph Conrad’s novel The Rover.

Speech emotion recognition (SER) is a complicated and challenging task in the human-computer interaction because it is difficult to find the best feature set to discriminate the emotional state entirely. We always used the FFT to handle the raw signal in the process of extracting the low-level description features, such as short-time energy, fundamental frequency, formant, MFCC (mel frequency cepstral coefficient) and so on. However, these features are built on the domain of frequency and ignore the information from temporal domain. In this paper, we propose a novel framework that utilizes multi-layers wavelet sequence set from wavelet packet reconstruction (WPR) and conventional feature set to constitute mixed feature set for achieving the emotional recognition with recurrent neural networks (RNN) based on the attention mechanism. In addition, the silent frames have a disadvantageous effect on SER, so we adopt voice activity detection of autocorrelation function to eliminate the emotional irrelevant frames. We show that the application of proposed algorithm significantly outperforms traditional features set in the prediction of spontaneous emotional states on the IEMOCAP corpus and EMODB database respectively, and we achieve better classification for both speaker-independent and speaker-dependent experiment. It is noteworthy that we acquire 62.52% and 77.57% accuracy results with speaker-independent (SI) performance, 66.90% and 82.26% accuracy results with speaker-dependent (SD) experiment in final.