Early language acquisition is vital for child cognitive and social development. Timely identification of language delays enables effective interventions. Traditional Language Sample Analysis (LSA) methods, the gold standard for assessing language acquisition, are time-consuming, making automation a promising alternative. Yet, analyzing spontaneous speech for linguistic features is hard due to errors from spoken language processing (SLP) systems. This study uses the KidsTALC dataset to explore the link between SLP performance and LSA measures of early language acquisition. We use a child-adapted ASR model to assess LSA measures, focusing on lexical diversity, on both human and machine transcripts. Our findings show that some LSA measures, like HD-D and vocd-D, are robust to imperfect ASR, with correlations over 0.9. This suggests that ASR-driven LSA, with proper methods and metric selection, can offer valuable insights for early language evaluation and aid in therapy decisions.

Human affect recognition is an essential part of natural human-computer interaction. However, current methods are still in their infancy, especially for in-the-wild data. In this work, we introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a two-stream aural-visual analysis model to recognize affective behavior from videos. Audio and image streams are first processed separately and fed into a convolutional neural network. Instead of applying recurrent architectures for temporal analysis we only use temporal convolutions. Furthermore, the model is given access to additional features extracted during face-alignment. At training time, we exploit correlations between different emotion representations to improve performance. Our model achieves promising results on the challenging Aff-Wild2 database.