The rapid expansion of online content has intensified the issue of information redundancy, underscoring the need for solutions that can identify genuinely new information. Despite this challenge, the research community has seen a decline in focus on novelty detection, particularly with the rise of large language models (LLMs). Additionally, previous approaches have relied heavily on human annotation, which is time-consuming, costly, and particularly challenging when annotators must compare a target document against a vast number of historical documents. In this work, we introduce NovAScore (Novelty Evaluation in Atomicity Score), an automated metric for evaluating document-level novelty. NovAScore aggregates the novelty and salience scores of atomic information, providing high interpretability and a detailed analysis of a document’s novelty. With its dynamic weight adjustment scheme, NovAScore offers enhanced flexibility and an additional dimension to assess both the novelty level and the importance of information within a document. Our experiments show that NovAScore strongly correlates with human judgments of novelty, achieving a 0.626 Point-Biserial correlation on the TAP-DLND 1.0 dataset and a 0.920 Pearson correlation on an internal human-annotated dataset.

This paper presents a novel dataset (CORAAL QA) and framework for audio question-answering from long audio recordings containing 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 Regional African American Language. Using this dataset, we divide the audio recordings into 60 second segments, automatically transcribe each segment, and use PLDA scoring of BERT-based semantic 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 examples 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 paper1presents 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.

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.

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.

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 Com-ParE 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 lowdimensional 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.

Theories of discourse coherence posit relations between discourse segments as a key feature of coherent text.Our prior work suggests that multiple discourse relations can be simultaneously operative between two segments for reasons not predicted by the literature.Here we test how this joint presence can lead participants to endorse seemingly divergent conjunctions (e.g., but and so) to express the link they see between two segments.These apparent divergences are not symptomatic of participant naïveté or bias, but arise reliably from the concurrent availability of multiple relations between segments -some available through explicit signals and some via inference.We believe that these new results can both inform future progress in theoretical work on discourse coherence and lead to higher levels of performance in discourse parsing.

The method of phylogenetic ancestral sequence reconstruction is a powerful approach for studying evolutionary relationships among protein sequence, structure, and function. In particular, this approach allows investigators to (1) reconstruct and "resurrect" (that is, synthesize in vivo or in vitro) extinct proteins to study how they differ from modern proteins, (2) identify key amino acid changes that, over evolutionary timescales, have altered the function of the protein, and (3) order historical events in the evolution of protein function. Widespread use of this approach has been slow among molecular biologists, in part because the methods require significant computational expertise. Here we present PhyloBot, a web-based software tool that makes ancestral sequence reconstruction easy. Designed for non-experts, it integrates all the necessary software into a single user interface. Additionally, PhyloBot provides interactive tools to explore evolutionary trajectories between ancestors, enabling the rapid generation of hypotheses that can be tested using genetic or biochemical approaches. Early versions of this software were used in previous studies to discover genetic mechanisms underlying the functions of diverse protein families, including V-ATPase ion pumps, DNA-binding transcription regulators, and serine/threonine protein kinases. PhyloBot runs in a web browser, and is available at the following URL: http://www.phylobot.com. The software is implemented in Python using the Django web framework, and runs on elastic cloud computing resources from Amazon Web Services. Users can create and submit jobs on our free server (at the URL listed above), or use our open-source code to launch their own PhyloBot server.