Joachim Schenk

The Godfather vs. Chaos: Comparing Linguistic Analysis Based on On-line Knowledge Sources and Bags-of-N-Grams for Movie Review Valence Estimation

In the fields of sentiment and emotion recognition, bag of words modeling has lately become popular for the estimation of valence in text. A typical application is the evaluation of reviews of e. g. movies, music, or games. In this respect we suggest the use of back-off N-Grams as basis for a vector space construction in order to combine advantages of word-order modeling and easy integration into potential acoustic feature vectors intended for spoken document retrieval. For a fine granular estimate we consider data-driven regression next to classification based on Support Vector Machines. Alternatively the on-line knowledge sources ConceptNet, General Inquirer, and WordNet not only serve to reduce out-of-vocabulary events, but also as basis for a purely linguistic analysis. As special benefit, this approach does not demand labeled training data. A large set of 100 k movie reviews of 20 years stemming from Metacritic is utilized throughout extensive parameter discussion and comparative evaluation effectively demonstrating efficiency of the proposed methods.

Novel VQ Designs for Discrete HMM On-Line Handwritten Whiteboard Note Recognition

In this work we propose two novel vector quantization (VQ) designs for discrete HMM-based on-line handwriting recognition of whiteboard notes. Both VQ designs represent the binary pressure information without any loss. The new designs are necessary because standard k-means VQ systems cannot quantize this binary feature adequately, as is shown in this paper. Our experiments show that the new systems provide a relative improvement of r= 1.8 % in recognition accuracy on a character- and r= 3.3 % on a word-level benchmark compared to a standard k-means VQ system. Additionally, our system is compared and proven to be competitive to a state-of-the-art continuous HMM-based system yielding a slight relative improvement of r= 0.6 %.

Optimizing the Number of States for HMM-Based On-line Handwritten Whiteboard Recognition

In this paper, we present a novel way to determine the number of states in Hidden-Markov-Models for on-line handwriting recognition. This method extends the Bakis length modeling method which has succesfully been applied to off-line handwriting recognition. We propose a modification to the Bakis method and present a technique to improve the topology with a small number of iterations. Furthermore, we investigate the influence of state tying. In an experimental section, we show that our improved system outperforms a system with Bakis length modeling by 1.5 % relative and with fixed length modeling by 5.1 % relative on the IAM-On-DB-t1 benchmark.

Automatic Multi-Modal Meeting Camera Selection for Video-Conferences and Meeting Browsers

In a video-conference the participants usually see the video of the speaker. However if somebody reacts (e. g. nodding) the system should switch to his video. Current systems do not support this. We formulate this camera selection as a pattern recognition problem. Then we apply HMMs to learn this behaviour. Thus our system can easily be adapted to different meeting scenarios. Furthermore, while current systems stay on the speaker, our system will switch if somebody reacts. In an experimental section we show that -compared to a desired output -a current system shows the wrong camera more than half of the time (frame error rate 53%), where our system selects the wrong camera in only a quarter of the time (FER 27%).

Robust Multi-Modal Group Action Recognition in Meetings from Disturbed Videos with the Asynchronous Hidden Markov Model

The asynchronous hidden Markov model (AHMM) models the joint likelihood of two observation sequences, even if the streams are not synchronised. We explain this concept and how the model is trained by the EM algorithm. We then show how the AHMM can be applied to the analysis of group action events in meetings from both clear and disturbed data. The AHMM outperforms an early fusion HMM by 5.7% recognition rate (a rel. error reduction of 38.5%) for clear data. For occluded data, the improvement is in average 6.5% recognition rate (rel. error red. 40%). Thus asynchronity is a dominant factor in meeting analysis, even if the data is disturbed. The AHMM exploits this and is therefore much more robust against disturbances.

A multi-agent framework for a hybrid dialog management system

The importance of dialog management systems has increased in recent years. Dialog systems are created for domain specific applications, so that a high demand for a flexible dialog system framework arises. There are two basic approaches for dialog management systems: a rule-based approach and a statistic approach. In this paper, we combine both methods and form a hybrid dialog management system in a scalable agent based framework. For deciding of the next dialog step, two independent systems are used: the Java Rule Engine (JESS) as expert system for rule-based solutions, and the Partially Observable Markov Decision Process (POMDP) as model-based solution for more complex dialog sequences. Using a speech recognizer and text-to-speech systems, the human can be guided through a dialog with approximately ten steps.

Neural net vector quantizers for discrete HMM-based on-line handwritten whiteboard-note recognition

In this work we evaluate a recently published vector quantization scheme, which has been developed to handle binary features like the pressure feature occurring in on-line handwriting recognition using discrete Hidden-Markov-Models (HMMs) with two neural net based vector quantizers (VQs). One of these uses a ldquoWinner-Take-Allrdquo (WTA) update rule and the other implements the ldquoNeural Gasrdquo (NG) approach. Both approaches are believed to be more efficient VQs than the standard k-means VQ used in our earlier publication. In an experimental section we prove that both the WTA and NG neural net VQ significantly (significance is measured by the one-sided t-test) outperform our previously used k-means VQ by rW = 0:9% and rN = 0:8%, respectively, referring to word-level accuracy. In addition, no significant difference in recognition accuracy between the WTA-VQ and the NG-VQ could be observed.

Natural Language Understanding by Combining Statistical Methods and Extended Context-Free Grammars

This paper introduces an novel framework for speech understanding using extended context-free grammars (ECFGs) by combining statistical methods and rule based knowledge. By only using 1st level labels a considerable lower expense of annotation effort can be achieved. In this paper we derive hierarchical non-deterministic automata from the ECFGs, which are transformed into transition networks (TNs) representing all kinds of labels. A sequence of recognized words is hierarchically decoded by using a Viterbi algorithm. In experiments the difference between a hand-labeled tree bank annotation and our approach is evaluated. The conducted experiments show the superiority of our proposed framework. Comparing to a hand-labeled baseline system (

GMs in On-Line Handwritten Whiteboard Note Recognition: The Influence of Implementation and Modeling

\widehat{=} 100\%

Selecting Features Using the SFS in Conjunction with Vector Quantization

) we achieve 95,4 % acceptance rate for complete sentences and 97.8 % for words. This induces an accuray rate of 95.1 % and error rate of 4.9 %, respectively F1-measure 95.6 % in a corpus of 1 300 sentences.