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  1. 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. 
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  2. 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. 
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    Free, publicly-accessible full text available April 1, 2025
  3. This paper evaluates the performance of widely-used open-source automatic speech recognition systems in transcribing primarily African American English-speaking children’s speech for educational applications. We investigate the performance of the Whisper, HuBERT, and Wav2Vec2 ASR systems as well as the capability of the transformer-based language model, BERT, for automatically grading the student’s oral responses to assessment prompts through use of the generated ASR transcripts. We achieve a 95% oral response scoring accuracy through the methods described. We also show a thorough analysis of ASR system performance over a diverse set of metrics going beyond the standard word error rate. 
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    Free, publicly-accessible full text available December 4, 2024
  4. 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. 
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    Free, publicly-accessible full text available August 20, 2024
  5. IEEE SIGNAL PROCESSING SOCIETY (Ed.)
    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. 
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  6. ABSTRACT

    Studying coupling between different galactic components is a challenging problem in galactic dynamics. Using basis function expansions (BFEs) and multichannel singular spectrum analysis (mSSA) as a means of dynamical data mining, we discover evidence for two multicomponent disc–halo dipole modes in a Milky-Way-like simulated galaxy. One of the modes grows throughout the simulation, while the other decays throughout the simulation. The multicomponent disc–halo modes are driven primarily by the halo, and have implications for the structural evolution of galaxies, including observations of lopsidedness and other non-axisymmetric structure. In our simulation, the modes create surface density features up to 10 per cent relative to the equilibrium model stellar disc. While the simulated galaxy was constructed to be in equilibrium, BFE + mSSA also uncovered evidence of persistent periodic signals incited by aphysical initial conditions disequilibrium, including rings and weak two-armed spirals, both at the 1 per cent level. The method is sensitive to distinct evolutionary features at and even below the 1 per cent level of surface density variation. The use of mSSA produced clean signals for both modes and disequilibrium, efficiently removing variance owing to estimator noise from the input BFE time series. The discovery of multicomponent halo–disc modes is strong motivation for application of BFE + mSSA to the rich zoo of dynamics of multicomponent interacting galaxies.

     
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  7. ISCA (Ed.)
    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. 
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  8. 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. 
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  9. 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. 
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