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A database of German emotional speech
The article describes a database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions, producing 10 German utterances (5 short and 5 longer sentences) which could be used inExpand
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Introduction
TLDR
The first meeting of focal points jointly convened by Costa Rica, Denmark and Ghana, in association with the Global Centre for the Responsibility to Protect, took place on 17 and 18 May 2011. Expand
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The INTERSPEECH 2010 paralinguistic challenge
TLDR
This paper introduces the conditions, the Challengecorpora “aGender” and “TUM AVIC”, and standard feature setsthat may be used. Expand
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The INTERSPEECH 2012 Speaker Trait Challenge
TLDR
We introduced the INTERSPEECH 2012 Speaker Trait Challenge that concentrated on perceived speaker traits. Expand
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Verification of acoustical correlates of emotional speech using formant-synthesis
This paper explores the perceptual relevance of acoustical correlates of emotional speech by means of speech synthesis. Besides, the research aims at the development of emotion-rules which enable anExpand
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Paralinguistics in speech and language - State-of-the-art and the challenge
TLDR
Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. Expand
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Age and gender recognition for telephone applications based on GMM supervectors and support vector machines
TLDR
This paper compares two approaches of automatic age and gender classification with 7 classes. Expand
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A Database of Age and Gender Annotated Telephone Speech
TLDR
This article describes an age-annotated database of German telephone speech. Expand
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EmotionML - An Upcoming Standard for Representing Emotions and Related States
TLDR
We describe the syntax of EmotionML as well as the vocabularies that are made available to describe emotions in terms of categories, dimensions, appraisals and/or action tendencies. Expand
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Comparison of Four Approaches to Age and Gender Recognition for Telephone Applications
TLDR
This paper presents a comparative study of four different approaches to automatic age and gender classification using seven classes on telephony speech task and also compares the results with human performance on the same data. Expand
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