Hearing & Speech Sciences Undergraduate Honors Theses

Permanent URI for this collectionhttp://hdl.handle.net/1903/29522

The objective of the HESP Honors Program is to encourage and recognize superior academic achievement and scholarship by providing opportunities for interested, capable, and energetic undergraduates to engage in independent study. A research project will be conducted under the supervision of a faculty mentor and will result in an Honors thesis.

The goals of the HESP Honors program are as follows:

  • Educate students to think independently on a broad range of ideas and issues related to the study of Hearing and Speech Sciences.
  • Provide opportunities for in-depth, scholarly, and scientific analysis of significant and current topics in the Hearing and Speech Sciences.
  • Provide students with the experience of undertaking a research project.

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Author: search.filters.author.Harvey, James

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    Modeling Language Development: How Machine Learning can Enhance Analysis of the Language Environment
    (2024-12-18) Harvey, James; Huang, Yi Ting; Newman, Rochelle; Domanski, Sophie
    Language sampling elicits a representative picture of a child’s language and provides methods for assessing functional communication beyond what is offered by standardized tests. Naturalistic sampling reduces time costs, and offers an ideal way to assess differences in home language associated with differences in socioeconomic status (SES). Unfortunately, naturalistic dense recordings present challenges in terms of how to scale analysis and extract meaningful information. This study investigates the application and analysis of the Language ENvironment Analysis system (LENA) for sampling home language using technology-assisted transcription and topic modeling. To evaluate the efficacy of transcription, segments were selected in reference to their amount of meaningful speech as measured by LENA, and transcribed by Whisper, OpenAI’s automatic speech recognition software. Research assistants trimmed text files to retain available adult language separated by utterance. Results suggest that this method of sampling, technology-assisted transcription, and automated analysis of traditional language metrics reproduces expected associations between parental input, SES, and standardized child vocabulary size. Topic models did not identify activity contexts, likely due to the nature of the input. This research presents a validated pipeline to produce dense representative data that utilizes modern approaches to reduce traditional time costs.