title: I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance.
abstract: We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient.
authors.affiliation: Machine Intelligence & Signal Processing Group, MMK, Technische Universität München, München, Germany.|Chair of Complex & Intelligent Systems, University of Passau, Passau, Germany.
authors.forename: Simone
authors.initials: S
authors.lastname: Hantke
authors.affiliation: Machine Intelligence & Signal Processing Group, MMK, Technische Universität München, München, Germany.
authors.forename: Felix
authors.initials: F
authors.lastname: Weninger
authors.affiliation: Machine Intelligence & Signal Processing Group, MMK, Technische Universität München, München, Germany.
authors.forename: Richard
authors.initials: R
authors.lastname: Kurle
authors.affiliation: Chair of Complex & Intelligent Systems, University of Passau, Passau, Germany.
authors.forename: Fabien
authors.initials: F
authors.lastname: Ringeval
authors.affiliation: Chair of Complex & Intelligent Systems, University of Passau, Passau, Germany.|Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
authors.forename: Anton
authors.initials: A
authors.lastname: Batliner
authors.affiliation: Chair of Complex & Intelligent Systems, University of Passau, Passau, Germany.
authors.forename: Amr El-Desoky
authors.initials: Ael-D
authors.lastname: Mousa
authors.affiliation: Chair of Complex & Intelligent Systems, University of Passau, Passau, Germany.|Department of Computing, Imperial College London, London, United Kingdom.
authors.forename: Björn
authors.initials: B
authors.lastname: Schuller
mesh_terms: D000328:Adult; D001296:Audiovisual Aids; D001331:Automation; D019992:Databases as Topic; D004435:Eating / Q000502:physiology*; D005260:Female; D005502:Food; D006309:Hearing / Q000502:physiology*; D006801:Humans; D008297:Male; D012044:Regression Analysis; D057566:Self Report; D013060:Speech / Q000502:physiology*; D049250:Speech Recognition Software; D060388:Support Vector Machine