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.
Chinese is a tonal language, which differentiates it from non-tonal languages in the Western countries. A Chinese character consists of an initial, a final, and a tone. In the present study, the effects of noise and reverberation on the Chinese syllable, initial, final, and tone identification in rooms were investigated by using simulated binaural impulse responses through auralization method. The results show that the syllable identification score is the lowest, the tone identification score is the highest, and the initial identification scores are lower than those of the final identification under the same reverberation time and signal-to-noise ratio condition. The Chinese syllable, initial, and final identification scores increase with the increase of signal-to-noise ratio and decrease of the reverberation time. The noise and reverberation have insignificant effects on the Chinese tone identification scores under most room acoustical environments. The statistical relationship between the Chinese syllable articulation and phoneme articulation had been experimentally proved under different noise and reverberation conditions in simulated rooms.
The article is devoted to the problem of voice signals recognition means introduction in the system of distance learning. The results of the conducted research determine the prospects of neural network means of phoneme recognition. It is also shown that the main difficulties of creation of the neural network model, intended for recognition of phonemes in the system of distance learning, are connected with the uncertain duration of a phoneme-like element. Due to this reason for recognition of phonemes, it is impossible to use the most effective type of neural network model on the basis of a multilayered perceptron, at which the number of input parameters is a fixed value. To mitigate this shortcoming, the procedure, allowing to transform the non-stationary digitized voice signal to the fixed quantity of mel-cepstral coefficients, which are the basis for calculation of input parameters of the neural network model, is developed. In contrast to the known ones, the possibility of linear scaling of phoneme-like elements is available in the procedure. The number of computer experiments confirmed expediency of the fact that the use of the offered coding procedure of input parameters provides the acceptable accuracy of neural network recognition of phonemes under near-natural conditions of the distance learning system. Moreover, the prospects of further research in the field of development of neural network means of phoneme recognition of a voice signal in the system of distance learning is connected with an increase in admissible noise level. Besides, the adaptation of the offered procedure to various natural languages, as well as to other applied tasks, for instance, a problem of biometric authentication in the banking sector, is also of great interest.