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This paper describes an original dataset of children's speech, collected through the use of JIBO, a social robot. The dataset encompasses recordings from 110 children, aged 4–7 years old, who participated in a letter and digit identification task and extended oral discourse tasks requiring explanation skills, totaling 21 h of session data. Spanning a 2-year collection period, this dataset contains a longitudinal component with a subset of participants returning for repeat recordings. The dataset, with session recordings and transcriptions, is publicly available, providing researchers with a valuable resource to advance investigations into child language development. 

This paper evaluates an innovative framework for spoken dialect density prediction on children's and adults' African American English. A speaker's dialect density is defined as the frequency with which dialect-specific language characteristics occur in their speech. Rather than treating the presence or absence of a target dialect in a user's speech as a binary decision, instead, a classifier is trained to predict the level of dialect density to provide a higher degree of specificity in downstream tasks. For this, self-supervised learning representations from HuBERT, handcrafted grammar-based features extracted from ASR transcripts, prosodic features, and other feature sets are experimented with as the input to an XGBoost classifier. Then, the classifier is trained to assign dialect density labels to short recorded utterances. High dialect density level classification accuracy is achieved for child and adult speech and demonstrated robust performance across age and regional varieties of dialect. Additionally, this work is used as a basis for analyzing which acoustic and grammatical cues affect machine perception of dialect. 

This paper presents a novel dataset (CORAAL QA) and framework for audio question-answering from long audio recordings contain- ing spontaneous speech. The dataset introduced here provides sets of questions that can be factually answered from short spans of a long audio files (typically 30min to 1hr) from the Corpus of Re- gional African American Language. Using this dataset, we divide the audio recordings into 60 second segments, automatically tran- scribe each segment, and use PLDA scoring of BERT-based seman- tic embeddings to rank the relevance of ASR transcript segments in answering the target question. In order to improve this framework through data augmentation, we use large language models including ChatGPT and Llama 2 to automatically generate further training ex- amples and show how prompt engineering can be optimized for this process. By creatively leveraging knowledge from large-language models, we achieve state-of-the-art question-answering performance in this information retrieval task. 

This work proposes a novel framework for automatically scor- ing children’s oral narrative language abilities. We use audio recordings from 3rd-8th graders of the Atlanta, Georgia area as they take a portion of the Test of Narrative Language. We de- sign a system which extracts linguistic features and fine-tuned BERT-based self-supervised learning representation from state- of-the-art ASR transcripts. We predict manual test scores from the extracted features. This framework significantly outper- forms a deterministic method based on the assessment’s scoring rubric. Last, we evaluate the system performance across stu- dent’s reading level, dialect, and diagnosed learning/language disabilities to establish fairness across diverse demographics of students. Using this system, we achieve approximately 98% classification accuracy of student scores. We are also able to identify key areas of improvement for this type of system across demographic areas and reading ability. 

This paper 1 presents a novel system which utilizes acoustic, phonological, morphosyntactic, and prosodic information for binary automatic dialect detection of African American English. We train this system utilizing adult speech data and then evaluate on both children’s and adults’ speech with unmatched training and testing scenarios. The proposed system combines novel and state-of-the-art architectures, including a multi-source transformer language model pre-trained on Twitter text data and fine-tuned on ASR transcripts as well as an LSTM acoustic model trained on self-supervised learning representations, in order to learn a comprehensive view of dialect. We show robust, explainable performance across recording conditions for different features for adult speech, but fusing multiple features is important for good results on children’s speech. 

In this paper, we explore automatic prediction of dialect density of the African American English (AAE) dialect, where dialect density is defined as the percentage of words in an utterance that contain characteristics of the non-standard dialect. We investigate several acoustic and language modeling features, including the commonly used X-vector representation and ComParE feature set, in addition to information extracted from ASR transcripts of the audio files and prosodic information. To address issues of limited labeled data, we use a weakly supervised model to project prosodic and X-vector features into low-dimensional task-relevant representations. An XGBoost model is then used to predict the speaker's dialect density from these features and show which are most significant during inference. We evaluate the utility of these features both alone and in combination for the given task. This work, which does not rely on hand-labeled transcripts, is performed on audio segments from the CORAAL database. We show a significant correlation between our predicted and ground truth dialect density measures for AAE speech in this database and propose this work as a tool for explaining and mitigating bias in speech technology. 

This paper proposes a novel linear prediction coding-based data augmentation method for children’s low and zero resource dialect ASR. The data augmentation procedure consists of perturbing the formant peaks of the LPC spectrum during LPC analysis and reconstruction. The method is evaluated on two novel children’s speech datasets with one containing California English from the Southern California Area and the other containing a mix of Southern American English and African American English from the Atlanta, Georgia area. We test the proposed method in training both an HMM-DNN system and an end-to-end system to show model-robustness and demonstrate that the algorithm improves ASR performance, especially for zero resource dialect children’s task, as compared to common data augmentation methods such as VTLP, Speed Perturbation, and SpecAugment. 

This paper presents the results of a pilot study that introduces social robots into kindergarten and first-grade classroom tasks. This study aims to understand 1) how effective social robots are in administering educational activities and assessments, and 2) if these interactions with social robots can serve as a gateway into learning about robotics and STEM for young children. We administered a commonly-used assessment (GFTA3) of speech production using a social robot and compared the quality of recorded responses to those obtained with a human assessor. In a comparison done between 40 children, we found no significant differences in the student responses between the two conditions over the three metrics used: word repetition accuracy, number of times additional help was needed, and similarity of prosody to the assessor. We also found that interactions with the robot were successfully able to stimulate curiosity in robotics, and therefore STEM, from a large number of the 164 student participants. 

Abstract In eukaryotes, clathrin-coated vesicles (CCVs) facilitate the internalization of material from the cell surface as well as the movement of cargo in post-Golgi trafficking pathways. This diversity of functions is partially provided by multiple monomeric and multimeric clathrin adaptor complexes that provide compartment and cargo selectivity. The adaptor-protein assembly polypeptide-1 (AP-1) complex operates as part of the secretory pathway at the trans-Golgi network (TGN), while the AP-2 complex and the TPLATE complex jointly operate at the plasma membrane to execute clathrin-mediated endocytosis. Key to our further understanding of clathrin-mediated trafficking in plants will be the comprehensive identification and characterization of the network of evolutionarily conserved and plant-specific core and accessory machinery involved in the formation and targeting of CCVs. To facilitate these studies, we have analyzed the proteome of enriched TGN/early endosome-derived and endocytic CCVs isolated from dividing and expanding suspension-cultured Arabidopsis (Arabidopsis thaliana) cells. Tandem mass spectrometry analysis results were validated by differential chemical labeling experiments to identify proteins co-enriching with CCVs. Proteins enriched in CCVs included previously characterized CCV components and cargos such as the vacuolar sorting receptors in addition to conserved and plant-specific components whose function in clathrin-mediated trafficking has not been previously defined. Notably, in addition to AP-1 and AP-2, all subunits of the AP-4 complex, but not AP-3 or AP-5, were found to be in high abundance in the CCV proteome. The association of AP-4 with suspension-cultured Arabidopsis CCVs is further supported via additional biochemical data.