A Cognitive Science Reasoning in Recognition of Emotions in Audio-Visual Speech

In this report we summarize the state-of-the-art of speech emotion recognition from the signal processing point of view. On the bases of multi-corporal experiments with machine-learning classifiers, the observation is made that existing approaches for supervised machine learning lead to database dependent classifiers which can not be applied for multi-language speech emotion recognition without additional training because they discriminate the emotion classes following the used training language. As there are experimental results showing that Humans can perform language independent categorisation, we made a parallel between machine recognition and the cognitive process and tried to discover the sources of these divergent results. The analysis suggests that the main difference is that the speech perception allows extraction of language independent features although language dependent features are incorporated in all levels of the speech signal and play as a strong discriminative function in human perception. Based on several results in related domains, we have suggested that in addition, the cognitive process of emotion-recognition is based on categorisation, assisted by some hierarchical structure of the emotional categories, existing in the cognitive space of all humans. We propose a strategy for developing language independent machine emotion recognition, related to the identification of language independent speech features and the use of additional information from visual (expression) features.

Organization and Security of the Audio and Video Archive for Unique Bulgarian Bells

AMS Subj. Classification: H.3.7 Digital Libraries, K.6.5 Security and Protection

GUIDE@HAND: Digital GPS Based Audio Guide that Brings the Past to Life

In the digital age the internet and the ICT devices changed our daily life and routines. It means we couldn't live without these services and devices anywhere (work, home, holiday, etc.). It can be experienced in the tourism sector; digital contents become key tools in the tourism of the 21st century; they will be able to adapt the traditional tourist guide methodology to the applications running on novel digital devices. Tourists belong to a new generation, an "ICT generation" using innovative tools, a new info-media to communicate. A possible direction for tourism development is to use modern ICT systems and devices. Besides participating in classical tours guided by travel guides, there is a new opportunity for individual tourists to enjoy high quality ICT based guided walks prepared on the knowledge of travel guides. The main idea of the GUIDE@HAND service is to use reusable, and create new tourism contents for an advanced mobile device, in order to give a contemporary answer to traditional systems of tourism information, by developing new tourism services based on digital contents for innovative mobile applications. The service is based on a new concept of enhancing territorial heritage and values, through knowledge, innovation, languages and multilingual solutions going along with new tourists‟ “sensitiveness”.

Accent Recognition for Noisy Audio Signals

It is well established that accent recognition can be as accurate as up to 95% when the signals are noise-free, using feature extraction techniques such as mel-frequency cepstral coefficients and binary classifiers such as discriminant analysis, support vector machine and k-nearest neighbors. In this paper, we demonstrate that the predictive performance can be reduced by as much as 15% when the signals are noisy. Specifically, in this paper we perturb the signals with different levels of white noise, and as the noise become stronger, the out-of-sample predictive performance deteriorates from 95% to 80%, although the in-sample prediction gives overly-optimistic results. ACM Computing Classification System (1998): C.3, C.5.1, H.1.2, H.2.4., G.3.