Publications

Abstract

Native listeners benefit from both visible speech and iconic gestures to enhance degraded speech comprehension (Drijvers & Ozyürek, 2017). We tested how highly proficient non-native listeners benefit from these visual articulators compared to native listeners. We presented videos of an actress uttering a verb in clear, moderately, or severely degraded speech, while her lips were blurred, visible, or visible and accompanied by a gesture. Our results revealed that unlike native listeners, non-native listeners were less likely to benefit from the combined enhancement of visible speech and gestures, especially since the benefit from visible speech was minimal when the signal quality was not sufficient.

Abstract

In human face-to-face communication, speech is frequently accompanied by visual signals, especially communicative hand gestures. Analyzing these visual signals requires detailed manual annotation of video data, which is often a labor-intensive and time-consuming process. To facilitate this process, we here present SPUDNIG (SPeeding Up the Detection of Non-iconic and Iconic Gestures), a tool to automatize the detection and annotation of hand movements in video data. We provide a detailed description of how SPUDNIG detects hand movement initiation and termination, as well as open-source code and a short tutorial on an easy-to-use graphical user interface (GUI) of our tool. We then provide a proof-of-principle and validation of our method by comparing SPUDNIG’s output to manual annotations of gestures by a human coder. While the tool does not entirely eliminate the need of a human coder (e.g., for false positives detection), our results demonstrate that SPUDNIG can detect both iconic and non-iconic gestures with very high accuracy, and could successfully detect all iconic gestures in our validation dataset. Importantly, SPUDNIG’s output can directly be imported into commonly used annotation tools such as ELAN and ANVIL. We therefore believe that SPUDNIG will be highly relevant for researchers studying multimodal communication due to its annotations significantly accelerating the analysis of large video corpora.