Voice Identification Using Classification Algorithms

This article discusses the classification algorithms for the problem of personality identification by voice using machine learning methods. We used the MFCC algorithm in the speech preprocessing process. To solve the problem, a comparative analysis of five classification algorithms was carried out. In the first experiment, the support vector method was determined—0.90 and multilayer perceptron—0.83, that showed the best results. In the second experiment, a multilayer perceptron with an accuracy of 0.93 was proposed using the Robust scaler method for personal identification. Therefore, to solve this problem, it is possible to use a multi-layer perceptron, taking into account the specifics of the speech signal

ZARZĄDZANIE MIEJSCEM PRACY ZA POMOCĄ BADAŃ OPERACYJNYCH

The optimal location of workplaces plays an important role in the structure of occupational safety. The design of the workspace should ensure the optimal distribution of functions between person and machine in order to create safe working conditions, reduce the severity of work and the level of production injuries. Most often, workplace planning is carried out manually, by simple calculation, and then the rationality of workplace planning is evaluated, based on statistics of industrial accidents and occupational diseases, as well as indicators of labor productivity, for example, the ratio of compliance with norms. To solve the problem of optimal placement in the work mathematical models are built that can take into account various regulatory restrictions and are simple for further software implementation. It is proposed to choose the theory of φ-functions as a basis, which can be characterized as measures of proximity of objects. Thus, the set task of optimal placement of workplaces is reduced to the task of mathematical programming. The objective function determines the criterion of optimality – the minimization of the area or perimeter that will be occupied by the objects. This formulation of the problem is relevant because the use of the smallest production area, taking into account safety requirements, is an economic condition for effective production management. The constraint on the relative location of workplaces is set using φ-functions, which defines the decision domain. That, when formalizing restrictions, you can take into account all regulatory safety distances between workplaces, equipment, walls, etc. Thus, the work explores an approach that will allow automatic planning of the placement of a large number of technological objects, workplaces in accordance with occupational safety standards. Use of the software application, which can be implemented on the basis of the φ-functions apparatus, will significantly reduce the time of workplaces planning and increase its efficiency.W strukturze ochrony pracy ważną rolę odgrywa optymalna organizacja miejsc pracy. Projektowanie przestrzeni roboczej powinno zapewnić optymalny podział funkcji pomiędzy człowieka i maszynę w celu stworzenia bezpiecznych warunków pracy, zmniejszenia uciążliwości pracy i poziomu urazów odniesionych w pracy. Najczęściej planowanie miejsca pracy odbywa się ręcznie, poprzez proste obliczenia, a następnie ocenia się racjonalność planowania miejsca pracy na podstawie statystyk urazów i chorób zawodowych, a także wskaźników wydajności pracy, na przykład współczynnika zgodności z normami. Do rozwiązania problemu optymalnego rozmieszczenia budowane są modele matematyczne, które mogą uwzględniać różne ograniczenia normatywne i są proste do dalszej implementacji programowej. Proponuje się wybór teorii funkcji φ, którą można scharakteryzować jako miarę bliskości obiektów. W ten sposób problem optymalnego rozmieszczenia miejsc pracy sprowadza się do problemu programowania matematycznego. Funkcja celu określa kryterium optymalności – minimalizację obszaru lub obwodu, który ma być zajęty przez obiekty. Takie postawienie problemu jest istotne, ponieważ wykorzystanie najmniejszej powierzchni produkcyjnej, z uwzględnieniem wymogów bezpieczeństwa, jest ekonomicznym warunkiem efektywnego zarządzania produkcją. Ograniczenia wzajemnej lokalizacji miejsc pracy ustalane są za pomocą funkcji φ, co określa domenę decyzyjną. Tak więc przy formalizowaniu ograniczeń można uwzględnić wszystkie normatywne odległości bezpieczeństwa między miejscami pracy, urządzeniami, ścianami itp. W związku z tym w artykule badane jest podejście, które będzie automatycznie planować rozmieszczenie dużej liczby obiektów technologicznych, miejsc pracy odpowiednio do standardów bezpieczeństwa pracy. Zastosowanie oprogramowania, które może być realizowane na bazie funkcji φ, znacznie skróci czas planowania miejsc roboczych i zwiększy jego efektywność

Continuous Speech Recognition of Kazakh Language

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained

Neural architectures for gender detection and speaker identification

In this paper, we investigate two neural architecture for gender detection and speaker identification tasks by utilizing Mel-frequency cepstral coefficients (MFCC) features which do not cover the voice related characteristics. One of our goals is to compare different neural architectures, multi-layers perceptron (MLP) and, convolutional neural networks (CNNs) for both tasks with various settings and learn the gender/speaker-specific features automatically. The experimental results reveal that the models using z-score and Gramian matrix transformation obtain better results than the models only use max-min normalization of MFCC. In terms of training time, MLP requires large training epochs to converge than CNN. Other experimental results show that MLPs outperform CNNs for both tasks in terms of generalization errors

Voice verification using i-vectors and neural networks with limited training data

This study proposes an approach to voice identification based on neural networks (DNN) for i-Vector. Modern voice identification systems based on DNN use large amounts of labeled training data. Using the LRE i-Vector Machine Learning Challenge restricts access to ready-to-use i-Vector for learning and testing the voice identification system. This poses unique challenges in developing DNN-based voice identification systems, since optimized external interfaces and network architectures can no longer be used. We propose to use the training i-Vectors to train the initial DNN to identify the voice. Next, we present a novel strategy for using this initial DNN to strip the language labels of the inappropriate set from the development data. The final DNN for voice identification is trained using the original training data and the estimated out-of-set language data. We show that augmenting the training set with out-of- set labels leads to a significant improvement in voice identification performance. In this paper, we studied the possibility of using neural networks for speech identification. In particular, standard approaches to speech recognition were considered, the concept of an artificial neuron as an object used in speech identification was defined. A speech recognition option using a neural network was investigated, and steps were presented to perform this task. Accuracy using neural networks with limited learning data and a higher i-vector dimension is superior to others with a score of 92.1%. From this study, we can conclude that the size of the UBM and the dimension of the i-vector affect the accuracy of voice identification based on the i-vector

Continuous Speech Recognition of Kazakh Language

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained