Search Results for author: Klaus Zechner

Found 15 papers, 0 papers with code

Do face masks introduce bias in speech technologies? The case of automated scoring of speaking proficiency

no code implementations17 Aug 2020 Anastassia Loukina, Keelan Evanini, Matthew Mulholland, Ian Blood, Klaus Zechner

However, these differences do not lead to differences in human or automated scores of English language proficiency.

The many dimensions of algorithmic fairness in educational applications

no code implementations WS 2019 Anastassia Loukina, Nitin Madnani, Klaus Zechner

We illustrate that total fairness may not be achievable and that different definitions of fairness may require different solutions.

BIG-bench Machine Learning Fairness

Using Rhetorical Structure Theory to Assess Discourse Coherence for Non-native Spontaneous Speech

no code implementations WS 2019 Xinhao Wang, Binod Gyawali, James V. Bruno, Hillary R. Molloy, Keelan Evanini, Klaus Zechner

This study aims to model the discourse structure of spontaneous spoken responses within the context of an assessment of English speaking proficiency for non-native speakers.

Using exemplar responses for training and evaluating automated speech scoring systems

no code implementations WS 2018 Anastassia Loukina, Klaus Zechner, James Bruno, Beata Beigman Klebanov

In this paper we compare the performance of an automated speech scoring engine using two corpora: a corpus of almost 700, 000 randomly sampled spoken responses with scores assigned by one or two raters during operational scoring, and a corpus of 16, 500 exemplar responses with scores reviewed by multiple expert raters.

Discourse Annotation of Non-native Spontaneous Spoken Responses Using the Rhetorical Structure Theory Framework

no code implementations ACL 2017 Xinhao Wang, James Bruno, Hillary Molloy, Keelan Evanini, Klaus Zechner

Considering that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we initiated a research effort to obtain RST annotations of a large number of non-native spoken responses from a standardized assessment of academic English proficiency.

Machine Translation Text Generation +1

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