This paper addresses the problem of statistical machine translation between highly inflected languages. Even when dealing with closely-related language pairs, statistical machine translation encounters problems if the parallel corpus is not big enough. To reduce the problem of data sparsity, we use the approach called factored translation, which has proven successful when translating between English and a morphologically rich language. We show that it is even more useful when translating between two highly inflected languages. The main contribution of the paper involves two extensions of the factored translation approach. First, we propose a new, more general asynchronous framework for training translation components, where lemmas in the lemma component and MSD tags in the MSD component are aligned independently of alignment done for surface word forms. The second contribution of the paper is a new technique for efficient use of a bilingual dictionary in the translation process. A dictionary is introduced into the lemma component to improve lexical translation. Dictionary use is based on entropy. We tested our enhanced translation approach on the Slovenian-Serbian language pair. The system was trained on a freely available OpenSubtitle corpus. The results show improvements in automatic scores (BLEU and TER). The approach could be used for other language pairs, especially if one or both are highly inflected.
COBISS.SI-ID: 17900054
In this chapter different factors that may influence Quality of Experience (QoE) in the context of networked services, media consumption, and other electronic communication based applications, are discussed. QoE can be subject to a range of complex and strongly interrelated factors, falling in three categories: into human, system and context influence factors (IFs). With respect to human IFs, we discuss variant and stable factors that may potentially bear an influence on QoE, either for low-level (bottom-up) or higher-level (top-down) cognitive processing. System IFs are classified into four distinct categories, namely content-, media-, network- and device-related IFs. Finally, the broad category of possible Context IFs is decomposed into factors linked to the physical, temporal, social, economic, task and technical information context. The overview given here illustrates the complexity of QoE and the broad range of aspects that potentially have a major influence on it.
COBISS.SI-ID: 17737238
Multimodal interfaces incorporating embodied conversational agents enable the development of novel concepts with regard to interaction management tactics in responsive human-machine interfaces. Such interfaces provide several additional nonverbal communication channels, such as natural visualized speech, facial expression, and different body motions. In order to simulate reactive human like communicative behavior and attitude, the realization of motion relies on different behavioral analyses and realization tactics and approaches. This article proposes a novel environment for online visual modeling of humanlike communicative behavior, named EVA-framework. In this study we focus on visual speech and nonverbal behavior synthesis by using hierarchical XML-based behavioral events and expressively adjustable motion templates. The main goal of the presented abstract motion notation scheme, named EVA-Script, is to enable the synthesis of unique and responsive behavior.
COBISS.SI-ID: 17861654
This paper presents a novel TTS-driven non-verbal behaviour system for co-verbal gesture synthesis. The system’s architecture and grammar, used to synchronize the non-verbal expressions with verbal information in symbolical and temporal domain, are presented in detail. The way how a visual representation of meaning can be selected, how the structure of its propagation can be generated as sequence movement-phases (based on lexical affiliation and semiotic rules), and how movement-phases and durations of movements can be aligned with the verbal content is also discussed. Finally, we explain how a procedural script is formed that drives the synchronized verbal and co-verbal behaviour. The generated synthetic behaviour already reflects a very high-degree of lip-sync and iconic, symbolic, and indexical expressions, as well as adaptors. As proven by the evaluation, most of the generated behaviour appears 'natural', and may adequately represent the verbal content.
COBISS.SI-ID: 17284886
Fingerprint enhancement is a key step in the Automated Fingerprint Identification System. Because of poor quality of a fingerprint the algorithm for feature extraction may extract features incorrectly, which affects incorrect fingerprint match and consequently inefficient fingerprint-based identity verification. Fingerprint image enhancement techniques are based on enhancement in spatial domain or in frequency domain or in a combination of both. This article presents a block-local normalization algorithm and a technique for speeding up a two-stage algorithm for low-quality fingerprint image enhancement with image learning, which first enhances a fingerprint image in the spatial domain and then in the frequency domain. The normalization technique includes an algorithm with block-local normalization with different block sizes. Experimental results obtained on a public database FVC2004 showed that the presented normalization technique speeds up and improves a state-of-the-art two-stage algorithm, provides better results in comparison with global and local normalization, and positively affects fingerprint image enhancement, and consequently improves the efficiency of the automated fingerprint identification system.
COBISS.SI-ID: 17967126