TY - GEN
T1 - Evaluation of the pain level from speech
T2 - 13th ITG Conference on Speech Communication
AU - Ren, Zhao
AU - Cummins, Nicholas
AU - Han, Jing
AU - Schnieder, Sebastian
AU - Krajewski, Jarek
AU - Schuller, Björn
N1 - Publisher Copyright:
© VDE VERLAG GMBH ∙ Berlin ∙ Offenbach
PY - 2020
Y1 - 2020
N2 - In many clinical settings, the evaluation of pain is achieved through a manual diagnostics procedure relying heavily on verbal descriptions from the patient. Such procedures can be time-consuming, costly, liable to subjective biases and therefore often inaccurate. The automatic evaluation of pain based on paralinguistic speech cues has the potential to enable objective methodologies for improving the objectivity and accuracy of pain diagnosis. In this regard, we herein introduce a novel audiovisual pain database, the Duesseldorf Acute Pain Corpus, in which 844 recordings were collected from 80 subjects whose speech was collected while they undertook a cold pressor pain induction paradigm. The database is split into speaker independent training/development/test sets for a three-class level of pain classification task and we provide a comprehensive set of benchmark experimental results. The feature representations tested include functionals and bag-of-audio-words from three feature sets: the Computational Paralinguistics Challenge (ComParE) features, mel-frequency ceptral coefficients, and deep spectrum representations. We use support vector machines and long short-term memory recurrent neural networks (LSTM-RNN) as the classifiers. The best result, 42.7 % unweighted average recall on the test set, is obtained by LSTM-RNN working on the deep spectrum representations.
AB - In many clinical settings, the evaluation of pain is achieved through a manual diagnostics procedure relying heavily on verbal descriptions from the patient. Such procedures can be time-consuming, costly, liable to subjective biases and therefore often inaccurate. The automatic evaluation of pain based on paralinguistic speech cues has the potential to enable objective methodologies for improving the objectivity and accuracy of pain diagnosis. In this regard, we herein introduce a novel audiovisual pain database, the Duesseldorf Acute Pain Corpus, in which 844 recordings were collected from 80 subjects whose speech was collected while they undertook a cold pressor pain induction paradigm. The database is split into speaker independent training/development/test sets for a three-class level of pain classification task and we provide a comprehensive set of benchmark experimental results. The feature representations tested include functionals and bag-of-audio-words from three feature sets: the Computational Paralinguistics Challenge (ComParE) features, mel-frequency ceptral coefficients, and deep spectrum representations. We use support vector machines and long short-term memory recurrent neural networks (LSTM-RNN) as the classifiers. The best result, 42.7 % unweighted average recall on the test set, is obtained by LSTM-RNN working on the deep spectrum representations.
UR - http://www.scopus.com/inward/record.url?scp=85094709511&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85094709511
T3 - Speech Communication - 13th ITG-Fachtagung Sprachkommunikation
SP - 56
EP - 60
BT - Speech Communication - 13th ITG-Fachtagung Sprachkommunikation
PB - VDE VERLAG GMBH
Y2 - 10 October 2018 through 12 October 2018
ER -