{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/yeah-right-uh-huh-a-deep-learning-backchannel","title":"Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor","arxiv_id":"1706.01340","date":"2017-06-02","proceeding":null,"authors":["Robin Ruede","Markus Müller","Sebastian Stüker","Alex Waibel"],"abstract":"Using supporting backchannel (BC) cues can make human-computer interaction\nmore social. BCs provide a feedback from the listener to the speaker indicating\nto the speaker that he is still listened to. BCs can be expressed in different\nways, depending on the modality of the interaction, for example as gestures or\nacoustic cues. In this work, we only considered acoustic cues. We are proposing\nan approach towards detecting BC opportunities based on acoustic input features\nlike power and pitch. While other works in the field rely on the use of a\nhand-written rule set or specialized features, we made use of artificial neural\nnetworks. They are capable of deriving higher order features from input\nfeatures themselves. In our setup, we first used a fully connected feed-forward\nnetwork to establish an updated baseline in comparison to our previously\nproposed setup. We also extended this setup by the use of Long Short-Term\nMemory (LSTM) networks which have shown to outperform feed-forward based setups\non various tasks. Our best system achieved an F1-Score of 0.37 using power and\npitch features. Adding linguistic information using word2vec, the score\nincreased to 0.39.","url_abs":"http://arxiv.org/abs/1706.01340v1","url_pdf":"http://arxiv.org/pdf/1706.01340v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"yeah-right-uh-huh-a-deep-learning-backchannel","repo_url":"https://github.com/phiresky/backchannel-prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}