skip to main content


Search for: All records

Creators/Authors contains: "Veeramani, Hariram"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  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. 
    more » « less
  2. 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. 
    more » « less
    Free, publicly-accessible full text available August 20, 2024