TY - GEN
T1 - Speech, emotion, age, language, task, and typicality
T2 - 2012 ASE/IEEE International Conference on Social Computing, SocialCom 2012 and the 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2012
AU - Marchi, Erik
AU - Batliner, Anton
AU - Schuller, Bjorn
AU - Fridenzon, Shimrit
AU - Tal, Shahar
AU - Golan, Ofer
PY - 2012
Y1 - 2012
N2 - The availability of speech corpora is positively correlated with typicality: The more typical the population is we draw our sample from, the easier it is to get enough data. The less typical the envisaged population is, the more difficult it is to get enough data. Children with Autism Spectrum Condition are atypical in several respect: They are children, they might have problems with an experimental setting where their speech should be recorded, and they belong to a specific subgroup of children. Thus we address two possible strategies: First, we analyse the feature relevance for samples taken from different populations, this is not directly improving performances but we found additional specific features within specific groups. Second, we perform cross-corpus experiments to evaluate if enriching the training data with data obtained from similar populations can increase classification performances. In this pilot study we therefore use four different samples of speakers, all of them producing one and the same emotion and in addition, the neutral state. We used two publicly available databases, the Berlin Emotional Speech database and the FAU Aibo Corpus, in addition to our own ASC-Inclusion database.
AB - The availability of speech corpora is positively correlated with typicality: The more typical the population is we draw our sample from, the easier it is to get enough data. The less typical the envisaged population is, the more difficult it is to get enough data. Children with Autism Spectrum Condition are atypical in several respect: They are children, they might have problems with an experimental setting where their speech should be recorded, and they belong to a specific subgroup of children. Thus we address two possible strategies: First, we analyse the feature relevance for samples taken from different populations, this is not directly improving performances but we found additional specific features within specific groups. Second, we perform cross-corpus experiments to evaluate if enriching the training data with data obtained from similar populations can increase classification performances. In this pilot study we therefore use four different samples of speakers, all of them producing one and the same emotion and in addition, the neutral state. We used two publicly available databases, the Berlin Emotional Speech database and the FAU Aibo Corpus, in addition to our own ASC-Inclusion database.
KW - Autism Spectrum conditions
KW - cross-corpus evaluation
KW - feature analysis
KW - speech emotion recognition
UR - http://www.scopus.com/inward/record.url?scp=84873653429&partnerID=8YFLogxK
U2 - 10.1109/SocialCom-PASSAT.2012.97
DO - 10.1109/SocialCom-PASSAT.2012.97
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AN - SCOPUS:84873653429
SN - 9780769548487
T3 - Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012
SP - 961
EP - 968
BT - Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012
Y2 - 3 September 2012 through 5 September 2012
ER -