Gender detection in children’s speech utterances for human-robot interaction

The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset

Optimization of automatic speech emotion recognition systems

Osnov za uspešnu integraciju emocionalne inteligencije u sofisticirane sisteme veštačke inteligencije jeste pouzdano prepoznavanje emocionalnih stanja, pri čemu se paralingvistički sadržaj govora izdvaja kao posebno značajan nosilac informacija o emocionalnom stanju govornika. U ovom radu je sprovedena komparativna analiza obeležja govornog signala i klasifikatorskih metoda najčešće korišćenih u rešavanju zadatka automatskog prepoznavanja emocionalnih stanja govornika, a zatim su razmotrene mogućnosti popravke performansi sistema za automatsko prepoznavanje govornih emocija. Izvršeno je unapređenje diskretnih skrivenih Markovljevih modela upotrebom QQ krive za potrebe određivanja etalona vektorske kvantizacije, a razmotrena su i dodatna unapređenja modela. Ispitane su mogućnosti vernije reprezentacije govornog signala, pri čemu je analiza proširena na veliki broj obeležja iz različitih grupa. Formiranje velikih skupova obeležja nameće potrebu za redukcijom dimenzija, gde je pored poznatih metoda analizirana i alternativna metoda zasnovana na Fibonačijevom nizu brojeva. Na kraju su razmotrene mogućnosti integracije prednosti različitih pristupa u jedinstven sistem za automatsko prepoznavanje govornih emocija, tako da je predložena paralelna multiklasifikatorska struktura sa kombinatornim pravilom koje pored rezultata klasifikacije pojedinačnih klasifikatora ansambla koristi i informacije o karakteristikama klasifikatora. Takođe, dat je predlog automatskog formiranja ansambla klasifikatora proizvoljne veličine upotrebom redukcije dimenzija zasnovane na Fibonačijevom nizu brojevaThe basis for the successful integration of emotional intelligence into sophisticated systems of artificial intelligence is the reliable recognition of emotional states, with the paralinguistic content of speech standing out as a particularly significant carrier of information regarding the emotional state of the speaker. In this paper, a comparative analysis of speech signal features and classification methods most often used for solving the task of automatic recognition of speakers' emotional states is performed, after which the possibilities for improving the performances of the systems for automatic recognition of speech emotions are considered. Discrete hidden Markov models were improved using the QQ plot for the purpose of determining the codevectors for vector quantization, and additional models improvements were also considered. The possibilities for a more faithful representation of the speech signal were examined, whereby the analysis was extended to a large number of features from different groups. The formation of big sets of features imposes the need for dimensionality reduction, where an alternative method based on the Fibonacci sequence of numbers was analyzed, alongside known methods. Finally, the possibilities for integrating the advantages of different approaches into a single system for automatic recognition of speech emotions are considered, so that a parallel multiclassifier structure is proposed with a combinatorial rule, which, in addition to the classification results of individual ensemble classifiers, uses information about classifiers' characteristics. A proposal is also given for the automatic formation of an ensemble of classifiers of arbitrary size by using dimensionality reduction based on the Fibonacci sequence of numbers

Развој кодера таласног облика за потребе неуронских мрежа и обраду сигнала

This doctoral thesis aims to design low-bit scalar quantizers and analyze their application in Neural Networks (NNs) and signal processing. In this thesis, we consider the possibilities and limitations that rest on quantization, as a leading technique for data coding and compression. In particular, we examine the inevitable accuracy loss of signal and data presentation due to quantization in the signal processing area, as well as in many modern solutions, that use quantization. As stated in this thesis, there are a number of qualitative performance indicators, which indicate that appropriate quantizer parameterization can optimize the amount of data transmitted in bits. Quantized Neural Networks (QNNs) is a promising research area, especially important for resource constrained devices. Relying on a plethora of conclusions about scalar quantizers derived for signal processing tasks and taking into account the advantages of scalar quantization, we anticipate that by studying the statistical characteristics of neural network parameters, this thesis will contribute to determining an efficient weights compression solution utilizing new, well-designed scalar quantizers for post-training quantization

Proyecto Docente e Investigador

PROYECTO DOCENTE E INVESTIGADOR Catedráticos de Universidad Área de Ciencia de la Computación e Inteligencia Artificial Universidad de Valladolid 19 de Mayo de 2023 David Escudero Manceb

Технология комплексной поддержки жизненного цикла семантически совместимых интеллектуальных компьютерных систем нового поколения

В издании представлено описание текущей версии открытой технологии онтологического проектирования, производства и эксплуатации семантически совместимых гибридных интеллектуальных компьютерных систем (Технологии OSTIS). Предложена стандартизация интеллектуальных компьютерных систем, а также стандартизация методов и средств их проектирования, что является важнейшим фактором, обеспечивающим семантическую совместимость интеллектуальных компьютерных систем и их компонентов, что существенное снижение трудоемкости разработки таких систем. Книга предназначена всем, кто интересуется проблемами искусственного интеллекта, а также специалистам в области интеллектуальных компьютерных систем и инженерии знаний. Может быть использована студентами, магистрантами и аспирантами специальности «Искусственный интеллект». Табл. 8. Ил. 223. Библиогр.: 665 назв