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De Ipina K.L.,University of the Basque Country | Hernandez C.,University of the Basque Country | Barroso N.,Irunweb Enterprise | Ezeiza A.,University of the Basque Country
Proceedings - International Carnahan Conference on Security Technology | Year: 2011

It Kokoro means, "heart" in Japanese. It also means "mind","soul", "sensibility", wishes and sentiments. All of these belong to the wide range of emotions and concepts that define the complex human condition. To be influenced by emotions is intrinsic to a human being. Emotions arise from having to face up to a changing and partially unpredictable world. This makes it necessary for all intelligent systems (whether natural or artificial) to develop emotions to survive [2]. Emotions are represented by the same subjective, cultural, physiological and behavioural components that express the perception of an individual's mental and physical state, and the way in which they interact with the environment. Then, how can we orientate Affective Computing? How can we fusion KOKORO and security context? Our system, KOKORO, analyses emotional expression based on ASR for Basque and Spanish users. Results are obtained over RekEmozio database [8] with SVM and HMMs. RekEmozio is a database of human emotions in Basque and Spanish (speech and video) with actors and amateurs. © 2011 IEEE.


Barroso N.,Irunweb Enterprise | De Ipina K.L.,University of the Basque Country | Hernandez C.,University of the Basque Country | Ezeiza A.,University of the Basque Country
Proceedings - International Carnahan Conference on Security Technology | Year: 2011

One of the goals of Speech Recognition Security (SRS) systems is to have appropriately tools to recognize speech password spoken based on elements such as words, sub-word or speakers. The main goal of the present work is to design robust ASR systems based on alternative ways to the classical evaluation rates, which often depend on the vocabulary of the task and on the language resources available. The drawback of this approach is that it is not straightforward that a system with a slightly lower WER during tests will adapt properly to new utterances, and this is much more sensible when the baseline system has a big error rate since there are many features that could be improved. This tends to be the case of under-resourced languages, since the lack of resources has a great impact in the performance of the system and not all the standard methods are suitable to any kind of language or task. The novel approach is to choose balanced multi-features of the acoustic models and the sub-word units based on rates related to entropy, mutual information and similitude. Selected models are integrated in an ontology-driven Audio Information Retrieval system that suits the requirements of under-resourced languages. © 2011 IEEE.


Barroso N.,Irunweb Enterprise | De Ipina K.L.,University of the Basque Country | Ezeiza A.,University of the Basque Country | Hernandez C.,University of the Basque Country
Proceedings - International Carnahan Conference on Security Technology | Year: 2011

It is significant to highlight that the demand of multilingual systems in human-computer interfaces is growing and automatic language identification (LID) is becoming increasingly important for the speech community as a fundamental tool to enrich theses systems. Research in this field has been active for several years. LID becomes also a useful resource in Multilingual Security System (MSS) providing methodologies to improve the robustness and the effectiveness of these systems. The main goal of our project is the development of Multilingual LVCSR systems in the Basque context. Nowadays, our work is oriented to the use of Basque in internet. The present work describes the development of a Language Identification (LID) system oriented to robust Multilingual Speech Recognition for under-resourced languages in the Basque context. © 2011 IEEE.


Barroso N.,Irunweb Enterprise | De Ipina K.L.,University of the Basque Country | Hernandez C.,University of the Basque Country | Ezeiza A.,University of the Basque Country
Proceedings - International Carnahan Conference on Security Technology | Year: 2011

The long term goal of our project is the development of robust Security Speech Recognition systems are based on Automatic Speech Recognition methodologies. The development of ASR systems involves dealing with issues such as Acoustic Phonetic Decoding (APD), Language Modelling (LM) or the development of appropriated Language Resources (LR). Thus these applications are generally very language-dependent and require very large resources. This work is focused to the selection of appropriated sub-word units with under-resourced and noisy conditions oriented to security tasks. The work has been carried out with a trilingual internet radio database. Thus, in order to decrease the negative impact that the lack of resources has in this issue we apply several data optimization methodologies based on Matrix Covariance Estimation and Ontology-based approaches. © 2011 IEEE.


Barroso N.,Irunweb Enterprise | De Ipina K.L.,University of the Basque Country | Ezeiza A.,University of the Basque Country | Hernandez C.,University of the Basque Country
Proceedings - International Carnahan Conference on Security Technology | Year: 2011

One of the goals of speech recognition security (SRS) systems could be to extract the subject of the discourse in order to detect possible risks. Thus, the development of appropriated Speech Semantic Recognition systems is one of the challenges the scientific community has. Our system is an Information Retrieval system that provides information about the contents of audio in Basque, Spanish, and French. Since the resources available for Basque in general, and for this task in particular, were very few, data optimization methodologies had to be applied in various phases of the development. Moreover, the agglutinative nature of Basque required the use of morphemes and other sub-word units. Additionally, some keyword spotting and semantic methods have been also applied in the system in order to retrieve information properly. In most of the cases, the methods employed during this project could suit the requirements of many under-resourced languages, and one of these techniques could be the ontology-based approach. This paper presents the system in general for Basque and emphasizes the techniques employed in order to enhance the system using a semantic ontology. © 2011 IEEE.


Barroso N.,Irunweb Enterprise | De Ipina K.L.,University of the Basque Country | Grana M.,University of the Basque Country | Hernandez C.,University of the Basque Country
Advances in Intelligent and Soft Computing | Year: 2011

The development of Multilingual Automatic Speech Recognition (ASR) systems involves Acoustic Phonetic Decoding, Language Modeling, Language Identification and the development of appropriated Language Resources. Only a small number of languages possess the resources required for these developments, the remaining languages are under-resourced. In this paper we explore robust strategies of Soft Computing in the selection of sub-word units oriented to under-resourced languages for ASR in the Basque context. Three languages are analyzed: French, Spanish and the minority one, Basque language. The proposed methodology is based on approaches of Discriminant and Principal Components Analysis, robust covariance matrix estimation methods, Support Vector Machines (SVM), Hidden Markov Models (HMMs) and cross-lingual strategies. New methods improve considerably the accuracy rate obtained on incomplete, small sample sets, providing an excellent tool to manage these kinds of languages. © 2011 Springer-Verlag Berlin Heidelberg.


Ezeiza A.,University of the Basque Country | De Ipina K.L.,University of the Basque Country | Hernandez C.,University of the Basque Country | Barroso N.,Irunweb Enterprise
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Hidden Markov Models and Mel Frequency Cepstral Coefficients (MFCC's) are a sort of standard for Automatic Speech Recognition (ASR) systems, but they fail to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is scarce, or when the ASR task is very complex. In this work, the Fractal Dimension (FD) of the observed time series is combined with the traditional MFCC's in the feature vector in order to enhance the performance of two different ASR systems: the first one is a very simple one, with very few training examples, and the second one is a Large Vocabulary Continuous Speech Recognition System for Broadcast News. © 2011 Springer-Verlag.


Barroso N.,Irunweb Enterprise | Lopez De Ipina K.,University of the Basque Country | Hernandez C.,University of the Basque Country | Ezeiza A.,University of the Basque Country | Grana M.,University of the Basque Country
International Journal of Speech Technology | Year: 2012

The long term goal of our project is the development of robust ASR systems in the Basque context where coexist French, Spanish and Basque (a minority language). The development of ASR systems involves dealing with issues such as Acoustic Phonetic Decoding (APD), Language Modelling (LM) or the development of appropriate Language Resources (LR). Thus, these applications are generally very language-dependent and require very large resources. This work is focused on the selection of appropriate sub-word units with under-resourced and noisy conditions. Nowadays, in particular, the work is oriented to Basque Broadcast News (BN) due to the interest of digital mass-media as the trilingual Infozazpi radio (situated in French Basque Country). Thus, in order to decrease the negative impact that the lack of resources has in this issue we apply several data optimization methodologies based on Matrix Covariance Estimation and Ontology-based approaches. © 2011 Springer Science+Business Media, LLC.


Barroso N.,Irunweb Enterprise | Lopez De Ipina K.,University of the Basque Country | Barroso O.,Irunweb Enterprise | Ezeiza A.,University of the Basque Country | And 2 more authors.
International Journal of Speech Technology | Year: 2012

This work, divided into Part I and II, describes the development of GorUP a Semantic Speech Recognition System in the Basque context. Part I analyses cross-lingual approaches oriented to under-resourced languages and Part II the development of the Language Identification system. During the development, data optimization methods and Soft Computing methodologies oriented to complex environment are used in order to overcome the lack of resources. Moreover, in this context three languages coexist: French, Spanish and Basque. Indeed our main goal is the development of robust Automatic Speech Recognition (ASR) systems for Basque, but all language variability has to be analyzed. In this regard, Basque speakers mix during the speech not only sounds but also words of the three languages which results in a strong presence of cross-lingual elements. Besides, Basque is an agglutinative language with a special morpho-syntactic structure inside the words that may lead to intractable vocabularies. Nowadays, our work is oriented to Information Retrieval and mainly to small internet mass-media. In these cases the available resources for Basque in general, and for this task in particular, are very few and complex to process because of the noisy environment. Thus, the methods employed in this development (ontology-based approach or cross-lingual methodologies oriented to profit from more powerful languages) could suit the requirements of many under-resourced languages. © 2011 Springer Science+Business Media, LLC.


Barroso N.,Irunweb Enterprise | De Ipina K.L.,University of the Basque Country | Ezeiza A.,University of the Basque Country | Barroso O.,Irunweb Enterprise | Susperregi U.,Irunweb Enterprise
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

The development of Multilingual Large Vocabulary Continuous Speech Recognition systems involves issues as: Language Identification, Acoustic-Phonetic Decoding, Language Modelling or the development of appropriated Language Resources. The interest on Multilingual Systems arouses because there are three official languages in the Basque Country (Basque, Spanish, and French), and there is much linguistic interaction among them, even if Basque has very different roots than the other two languages. This paper describes the development of a Language Identification (LID) system oriented to robust Multilingual Speech Recognition for the Basque context. The work presents hybrid strategies for LID, based on the selection of system elements by Support Vector Machines and Multilayer Perceptron classifiers and stochastic methods for speech recognition tasks (Hidden Markov Models and n-grams). © 2010 Springer-Verlag.

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