Kseniya Zablotskaya

Universität Ulm

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Wolfgang Minker

Universität Ulm

Sergey Zablotskiy

Universität Ulm

Fernando Fernández-Martínez

Universidad Politécnica de Madrid

Teerat Pitakrat

Monash University

Steffen Walter

University Hospital Ulm

Umair Rahim

Universität Ulm

Mumtaz Ahmed

Jamia Millia Islamia

Amr Diab

German University in Cairo

Mohsin Abbas

Tsinghua University

Florian Nothdurft

Universität Ulm

Cooperative Institutions

Universität Ulm

Information Technology University

University Hospital Ulm

Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie

Universidad Politécnica de Madrid

Technische Hochschule Ulm

University of Zurich

Klinik und Poliklinik für Psychiatrie und Psychotherapie

Amt für Umwelt

University of Bamberg

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Speech Data Corpus for Verbal Intelligence Estimation

Language Resources and Evaluation (2010)

Kseniya Zablotskaya Steffen Walter Wolfgang Minker

Computational linguistics

Text corpus

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Citations (7)

Optimal Operators of Hybrid Genetic Algorithm for GMM Parameter Estimation

Sergey Zablotskiy Teerat Pitakrat Kseniya Zablotskaya Wolfgang Minker

A genetic algorithm is an evolutionary algorithm that is widely used for solving global optimization problems. It generates the solution in the form of encoded binary chromosome using operators inspired by a natural evolution process: selection, crossover and mutation. In this paper, a hybrid genetic algorithm is applied to the emission probability estimation task of a continuous Hidden Markov Model which is one of the common optimization problems in speech recognition. Three backbone operators of the genetic algorithm are investigated in order to find the optimal Gaussian parameters that result in the best mixture model.

10.1109/ie.2011.60

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Some Approaches for Russian Speech Recognition

Sergey Zablotskiy Kseniya Zablotskaya Wolfgang Minker

In this paper we present an overview of the state-of-the-art approaches for speech recognition of the Russian language. Since Russian is a highly inflective language with a complex mechanism of word formation, the main approaches for English speech recognition are not optimally applicable for Russian speech, at least directly and without appropriate adaptation. We make here an overview of some modern methods designed specially for Russian and other languages with similar word formation mechanism and grammar basis. Among them there are morpheme based speech recognition and compound language modeling. We also describe here our direction for possible subsequent Russian speech recognizer improvements.

10.1109/ie.2010.25

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Citations (2)

Non-parametric Regression and Random Balance Method Modification for Determination of the Most Informative Features

Kseniya Zablotskaya Mumtaz Ahmed Sergey Zablotskiy Wolfgang Minker

In this paper we present a new method which allows us to detect the most informative features out of all data extracted from a certain data corpus. Widely used Pearson's coefficient is not reliable if the dependency between extracted features (input variables) and the objective function (output) is not linear. This approach is based on a modified random balance method (RBM) combined with non-parametric kernel regression for modeling the dependency between output and input variables. The standard random balance method stochastically determines the most important features of a process, but it requires the values of the objective function at the certain assigned points. If there is no possibility to calculate these values, it is necessary to approximate them. Since we assume that the dependency between stochastic variables can be non-linear, it is necessary to take an appropriate model. We used non-parametric kernel regression because knowledge about the parametric structure of the dependency is not needed. Moreover, we modified the random balance method to handle the non-linearity of the data.

Kernel (algebra)

Kernel regression

10.1109/ie.2010.19

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Citations (1)

Investigating Verbal Intelligence Using the TF-IDF Approach

Language Resources and Evaluation (2012)

Kseniya Zablotskaya Fernando Fernández-Martínez Wolfgang Minker

Similarity (geometry)

Cosine similarity

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Citations (2)

Text categorization methods for automatic estimation of verbal intelligence

Expert Systems with Applications (2012)

Fernando Fernández-Martínez Kseniya Zablotskaya Wolfgang Minker

10.1016/j.eswa.2012.02.173

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Citations (11)

GMM Parameter Estimation by Means of EM and Genetic Algorithms

Lecture notes in computer science (2011)

Sergey Zablotskiy Teerat Pitakrat Kseniya Zablotskaya Wolfgang Minker

Initialization

Forward algorithm

Maximization

10.1007/978-3-642-21602-2_57

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Citations (3)

Measuring Verbal Intelligence Using Linguistic Analysis

Kseniya Zablotskaya Mohsin Abbas Sergey Zablotskiy Steffen Walter Wolfgang Minker

In this paper we present a study on language use of people with different verbal intelligence. We asked test persons of different ages and educational background to describe the same event. Verbal intelligence was measured using the Hamburg Wechsler Intelligence Test for Adults. The transcribed monologues were analyzed using the DeLite readability checker and different linguistic features used for readability calculations were extracted. The test persons were then divided into two groups according to the results of the intelligence test using the SEM algorithm. For each group the averaged values of the features extracted from the monologues were compared using a one-way analysis of variance.

10.1109/ie.2011.62

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Citations (1)

Application of Verbal Intelligence in Dialog Systems for Multimodal Interaction

Florian Nothdurft Frank Honold Kseniya Zablotskaya Amr Diab Wolfgang Minker

In this paper we present a prototypical dialog system adaptive to verbal user intelligence. Verbal intelligence (VI) is the ability to analyze information and to solve problems using language-based reasoning. VI can be analyzed by the number of reused words, lemmas, n-grams, cosine similarity and other features. Here we concentrate on the application of VI in a human-computer interaction (HCI) and how this value can be used by the dialog management to adapt the dialog flow, and complexity at run-time. In our work complexity as well as informative value of presented information can be reduced or increased when encountering human-computer interaction by individually adapting to a lower or higher verbally intelligent user. Especially in intelligent environments, where user's may rely on speech as their primary interaction modality, the adaptation of system instructions to the the user's VI could prove helpful.

Dialog system

Similarity (geometry)

Modality (human–computer interaction)

10.1109/ie.2014.59

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Citations (5)

Estimating Adaptation of Dialogue Partners with Different Verbal Intelligence

Annual Meeting of the Special Interest Group on Discourse and Dialogue (2012)

Kseniya Zablotskaya Fernando Fernández-Martínez Wolfgang Minker

This work investigates to what degree speakers with different verbal intelligence may adapt to each other. The work is based on a corpus consisting of 100 descriptions of a short film (monologues), 56 discussions about the same topic (dialogues), and verbal intelligence scores of the test participants. Adaptation between two dialogue partners was measured using cross-referencing, proportion of I, You and We words, between-subject correlation and similarity of texts. It was shown that lower verbal intelligence speakers repeated more nouns and adjectives from the other and used the same linguistic categories more often than higher verbal intelligence speakers. In dialogues between strangers, participants with higher verbal intelligence showed a greater level of adaptation.

Similarity (geometry)

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