Fakultät für Informatik
Refine
Year of publication
- 2009 (5) (remove)
Document Type
Has Fulltext
- no (5)
Is part of the Bibliography
- no (5)
Keywords
- Auswertungsmethoden (1)
- Automatische Mustererkennung (1)
- Korrelation von Daten (1)
- Page Rank (1)
- Robotic Research (1)
- Software Development (1)
- Speech Recognition (1)
- Spracherkennungssoftware (1)
- Sprechverständlichkeit (1)
- Summarization (1)
Institute
In this paper, we present our experience in designing and teaching of our first robotics course for students at primary school level.
The course was carried out over a comparatively short period of time, namely 6 weeks, 2 hours per week. In contrast to many other projects, we use robots that researchers used to conduct their research and discuss problems faced by these researchers. Thus, this is not a behavioural study but a hands-on learning experience for the students.
The aim is to highlight the development of autonomous robots and artificial intelligence as well as to promote science and robotics in schools.
This paper presents an unsupervised, graph based approach for extractive summarization of meetings. Graph based methods such as TextRank have been used for sentence extraction from news articles. These methods model text as a graph with sentences as nodes and edges based on word overlap. A sentence node is then ranked according to its similarity with other nodes. The spontaneous speech in meetings leads to incomplete, informed sentences with high redundancy and calls for additional measures to extract relevant sentences. We propose an extension of the TextRank algorithm that clusters the meeting utterances and uses these clusters to construct the graph. We evaluate this method on the AM I meeting corpus and show a significant improvement over TextRank and other baseline methods.
We introduce a model for extractive meeting summarization based on the hypothesis that utterances convey bits of information, or concepts. Using keyphrases as concepts weighted by frequency, and an integer linear program to determine the best set of utterances, that is, covering as many concepts as possible while satisfying a length constraint, we achieve ROUGE scores at least as good as a ROUGE-based oracle derived from human summaries. This brings us to a critical discussion of ROUGE and the future of extractive meeting summarization.
Towards a Language-independent Intelligibility Assessment of Children with Cleft Lip and Palate
(2009)
We describe a novel evaluation system for the intelligibility assessment of children with CLP on standardized tests. The system is solely based on standard cepstral features in form of MFCCs. No other information like word alignments is used. So the system can be easily adapted to other languages. For each child one GMM is created by adaptation of a UBM to the speaker-specific MFCCs. The components of this GMM are concatenated in order to create a so-called GMM supervector. These GMM supervectors are then used as meta features for an SVR. We evaluated our language-independent system on two different datasets of children suffering from CLP. One dataset contains recordings of 35 German children, where the children named different pictograms. The other dataset contains recordings of 14 Italian speaking children, who repeated standardized sentences. On both datasets we achieved high correlations: up to 0.81 for the German dataset and 0.83 for the Italian dataset.
In dieser Studie wird ein objektives Verfahren für die Verständlichkeitsmessung mit dem Postlaryngektomie-Telefontest (PLTT) mittels automatischer Spracherkennungstechnik beschrieben.
31 Sprecher mit tracheoösophagealer Ersatzstimme (25 Männer und 6 Frauen; 63,4±8,7 Jahre) wurden zunächst von 11 naiven Hörern bewertet. Der vom Spracherkennungssystem ermittelte Verständlichkeitsgrad wird als Prozentsatz korrekt verstandener Wörter einer Wortkette, der Wortakkuratheit bzw. -korrektheit, angegeben und mit den subjektiv ermittelten PLTT-Werten verglichen.
Die durchschnittliche PLTT-Gesamtverständlichkeit der 11 naiven Hörer liegt bei 47%, die automatisch ermittelte Wortakkuratheit und Wortkorrektheit liegen deutlich niedriger (etwa 0% bzw. etwa 15%). Die Korrelation zwischen menschlicher und maschineller Bewertung liegt jedoch z. T. über 0,9.
Für den Gesamtverständlichkeitswert des PLTT kann mit Hilfe der automatischen Spracherkennung objektiv und effizient ein äquivalentes Maß berechnet werden.