PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions

Survey of deep representation learning for speech emotion recognition

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER

Klasifikace emocí v lidské řeči

Import 14/02/2017Dissertation thesis deals with recognition of the emotional state from human speech. The dissertation describes the current state of the Speech Emotion Recognition topic, deals with methods for speech features extraction, classification methods and is devoted to the design of a new system for speech emotion recognition. This system is modeled on the newly created emotional database emoDBova and the new database for stress detection 112DB. Designed speech emotion recognition system is implemented in secure communication infrastructure. The new databases are composed of spontaneous speech in the Czech language. The system for speech emotion recognition is designed on the basis of the last knowledge and to achieve higher accuracy than relevant proposals. The system is implemented to infrastructure, and its role is speech emotion recognition of phone call participants. Above mentioned newly created databases, a unique system for speech emotion recognition and its actual implementation in communications infrastructure are also major contributions of this work.Dizertačná práca sa zaoberá problematikou rozpoznania emočného stavu z reči človeka. Práca popisuje súčasný stav problematiky Speech Emotion Recognition, zaoberá sa metódami na extrakciu rečových príznakov, klasifikačnými metódami a je venovaná návrhu nového systému pre klasifikáciu emočného stavu z reči. Tento systém je namodelovaný na novovytvorenej emočnej databáze emoDBova a databáze pre detekciu stresu 112DB a implementovaný do infraštruktúry zabezpečeného komunikačného systému. Nové databázy sú vytvorené z spontánnej reči v českom jazyku. Systém pre rozpoznávanie emočného stavu je navrhnutý na základe posledných poznatkov a za účelom dosiahnutia vyššej presnosti ako prezentujú doterajšie návrhy. Celý systém je implementovaný do spomínanej infraštruktúry za účelom rozpoznávania emočného stavu účastníkov telefónneho rozhovoru. Spomínané novovytvorené databázy, unikátny systém pre rozpoznanie emočného stavu a jeho reálne nasadenie v komunikačnej infraštruktúre sú hlavnými prínosmi tejto práce.440 - Katedra telekomunikační technikyvyhově

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