Affective Dialogue Systems by Andri E. (Ed), Dybkjaer L. (Ed), Minker W. (Ed)

By Andri E. (Ed), Dybkjaer L. (Ed), Minker W. (Ed)

This booklet constitutes the refereed court cases of the overseas educational and examine Workshop on Affective discussion structures, advertisements 2004, held in Kloster Irsee, Germany in June 2004.The 21 revised complete papers and 14 revised brief papers provided have been conscientiously reviewed and chosen for presentation. The papers are geared up in topical sections on emotion acceptance; affective person modeling; emotional databases, annotation schemes, and instruments; affective conversational brokers and discussion platforms; synthesis of emotional speech and facial animations; affective tutoring platforms; evaluate of affective discussion platforms; and demonstrations.

Show description

Read Online or Download Affective Dialogue Systems PDF

Best nonfiction_1 books

A 5-local identification of the monster

Allow G be a in the community K-proper staff, S ∈ Syl_5(G), and Z = Z(S). We demonstratethat if is 5-constrained and Z isn't weakly closed in thenG is isomorphic to the monster sporadic basic team.

Extra info for Affective Dialogue Systems

Example text

The system works in push-to-talk mode and is also able to play speech files and then to classify them. This mode was used to evaluate the environmental conditions. 1 Intensity Intensity contour is an important feature for emotion recognition. Nevertheless, the raw energy contour has the drawback, that it is dependent on several factors, such as the microphone distance, the properties of the microphone and properties of the speaker’s voice. This makes normalization inevitable when going to reallife applications.

Possible workarounds are methods to localize the speaker by using several microphones and normalize by the distance or the use of close talk microphones. But these are restricting the range of possible applications more than necessary. In contrast, energy normalization allows the use of emotion detection systems in different environments. Noise is also influencing the performance of the classifier. 2, techniques from speech recognition like training with contaminated data, can compensate to some extent for the influence of additive noise.

These results support the assumption of different features related to different dimensions insofar, as more quality features are highranked for evaluation than prosodic features and vice versa. However, there are also some prosodic features high ranked in the evaluation decision and quality features in the activation dimension. This might be used in further classifiers, as most prosodic features are more reliable to calculate than the quality features. On the other hand, the results of feature selection, and thus the validity of particular features seem to depend a lot on how the speaker is expressing emotions: Towards Real Life Applications in Emotion Recognition 31 Fig.

Download PDF sample

Rated 4.59 of 5 – based on 32 votes