ASAAHMI 2014 Abstracts


Full Papers
Paper Nr: 1
Title:

Text Categorization Methods Application for Natural Language Call Routing

Authors:

Roman Sergienko, Tatiana Gasanova, Eugene Semenkin and Wolfgang Minker

Abstract: Natural language call routing can be treated as an instance of topic categorization of documents after speech recognition of calls. This categorization consists of two important parts. The first one is text preprocessing for numerical data extraction and the second one is classification with machine learning methods. This paper focuses on different text preprocessing methods applied for call routing. Different machine learning algorithms with several text representations have been applied for this problem. A novel text preprocessing technique has been applied and investigated. Numerical experiments have shown computational and classification effectiveness of the proposed method in comparison with standard techniques. Also a novel features selection method was proposed. The novel features selection method has demonstrated some advantages in comparison with standard techniques.
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Paper Nr: 2
Title:

Design Efficient Technologies for Context Image Analysis in Dialog HCI Using Self-Configuring Novelty Search Genetic Algorithm

Authors:

Evgeny Sopov and Ilia Ivanov

Abstract: The efficiency of HCI systems can be sufficiently improved by the analysis of additional contextual information about the human user and interaction process. The processing of visual context provides HCI with such information as user identification, age, gender, emotion recognition and others. In this work, an approach to adaptive model building for image classification is presented. The novelty search based upon the multi-objective genetic algorithm is used to stochastically design a variety of independent technologies, which provide different image analysis strategies. Finally, the ensemble based decision is built adaptively for the given image analysis problem.
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Paper Nr: 3
Title:

Russian Sub-Word Based Speech Recognition Using Pocketsphinx Engine

Authors:

Sergey Zablotskiy and Maxim Sidorov

Abstract: Russian is a synthetic language with a large morpheme-per-word ratio and highly inflective nature. These two peculiarities increase the lexicon size for Russian automatic speech recognition (ASR) by tens of times in comparison to that for English covering the same out-of-vocabulary (OOV) rate. The employment of sub-word units is a widely spread state-of-the-art approach to reduce the abundant lexicon and lower the perplexity (PP) of the language model. The choice of sub-word units affects the accuracy of the entire speech recognition system, its performance as well as the complexity of the spoken phrase synthesis. Here, different recognition units are investigated using pocketsphinx-engine while recognizing the vocabulary of several million word forms. A designed text normalization approach is also briefly presented. This rule-based algorithm allows keeping diverse Russian abbreviations and numerals in the language model (LM) and avoiding the statistics distortion. The approach is directly applicable and useful for Russian text-to-speech translation as well.
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Paper Nr: 4
Title:

Automatically Generated Classifiers for Opinion Mining with Different Term Weighting Schemes

Authors:

Shakhnaz Akhmedova, Eugene Semenkin and Roman Sergienko

Abstract: Automatically generated classifiers using different term weighting schemes for Opinion Mining are presented. New collective nature-inspired self-tuning meta-heuristic for solving unconstrained and constrained real- and binary-parameter optimization problems called Co-Operation of Biology Related Algorithms was developed and used for classifiers design. Three Opinion Mining problems from DEFT’07 competition were solved by proposed classifiers. Also different weighting schemes were used for data processing. Obtained results were compared between themselves and with results obtained by methods which were proposed by other researchers. As the result workability and usefulness of designed classifiers were established and best data processing approach for them was found.
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Paper Nr: 5
Title:

Acoustic Emotion Recognition - Two Ways of Features Selection based on Self-Adaptive Multi-Objective Genetic Algorithm

Authors:

Christina Brester, Maxim Sidorov and Eugene Semenkin

Abstract: In this paper the efficiency of feature selection techniques based on the evolutionary multi-objective optimization algorithm is investigated on the set of speech-based emotion recognition problems (English, German languages). Benefits of developed algorithmic schemes are demonstrated compared with Principal Component Analysis for the involved databases. Presented approaches allow not only to reduce the amount of features used by a classifier but also to improve its performance. According to the obtained results, the usage of proposed techniques might lead to increasing the emotion recognition accuracy by up to 29.37% relative improvement and reducing the number of features from 384 to 64.8 for some of the corpora.
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