Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Erzin, Engin"'
Efficient and Safe Contact-rich pHRI via Subtask Detection and Motion Estimation using Deep Learning
This paper proposes an adaptive admittance controller for improving efficiency and safety in physical human-robot interaction (pHRI) tasks in small-batch manufacturing that involve contact with stiff environments, such as drilling, polishing, cutting
Externí odkaz:
http://arxiv.org/abs/2407.14161
Autor:
Kuşçu, Gökhan, Erzin, Engin
Continuous emotion recognition (CER) aims to track the dynamic changes in a person's emotional state over time. This paper proposes a novel approach to translating CER into a prediction problem of dynamic affect-contour clusters from speech, where th
Externí odkaz:
http://arxiv.org/abs/2406.02569
Video summarization attracts attention for efficient video representation, retrieval, and browsing to ease volume and traffic surge problems. Although video summarization mostly uses the visual channel for compaction, the benefits of audio-visual mod
Externí odkaz:
http://arxiv.org/abs/2212.01040
Autor:
Safaya, Ali, Erzin, Engin
While the Turkish language is listed among low-resource languages, literature on Turkish automatic speech recognition (ASR) is relatively old. In this report, we present our findings on Turkish ASR with speech representation learning using HUBERT. We
Externí odkaz:
http://arxiv.org/abs/2210.07323
Humans' perception system closely monitors audio-visual cues during multiparty interactions to react timely and naturally. Learning to predict timing and type of reaction responses during human-human interactions may help us to enrich human-computer
Externí odkaz:
http://arxiv.org/abs/2206.10967
Autor:
Kopru, Berkay, Erzin, Engin
As speech-interfaces are getting richer and widespread, speech emotion recognition promises more attractive applications. In the continuous emotion recognition (CER) problem, tracking changes across affective states is an important and desired capabi
Externí odkaz:
http://arxiv.org/abs/2110.04091
Autor:
Köprü, Berkay, Erzin, Engin
Increasing volume of user-generated human-centric video content and their applications, such as video retrieval and browsing, require compact representations that are addressed by the video summarization literature. Current supervised studies formula
Externí odkaz:
http://arxiv.org/abs/2107.03783
Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., How are you?) might induce responses with different affects depending on the affective sta
Externí odkaz:
http://arxiv.org/abs/2012.06847
Autor:
Köprü, Berkay, Erzin, Engin
In this study, we focus on continuous emotion recognition using body motion and speech signals to estimate Activation, Valence, and Dominance (AVD) attributes. Semi-End-To-End network architecture is proposed where both extracted features and raw sig
Externí odkaz:
http://arxiv.org/abs/2011.00876
In human-to-computer interaction, facial animation in synchrony with affective speech can deliver more naturalistic conversational agents. In this paper, we present a two-stage deep learning approach for affective speech driven facial shape animation
Externí odkaz:
http://arxiv.org/abs/1908.03904