Speech Analytics in Research Based on Qualitative Interviews

Abstract

The paper presents aims and results of the project KA³ (Kölner Zentrum Analyse und Archivierung von audio-visual-Daten), in which advanced speech technologies are developed and provided to enhance the process of indexing and analysing speech recordings from the oral history domain and the language sciences. Close cooperation between speech technology scientists and digital humanities researchers is an important aspect of the project making sure that the development of the technologies answers the needs of research based on qualitative audio-visual interviews. For practical research reasons, the project focuses on the audio aspect, although visual aspects are of course equally important for the analysis of audio-visual data. The Cologne Centre for Analysis and Archiving of audio-visual data will provide the technologies as a central service.

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“…Two natural language processing (NLP) techniques (topic modeling and sentiment analysis) were applied to the collected interview transcriptions containing a significant amount of textual data (over 50,000 words) to reveal important latent information that was not able to be captured during the interview. NLP techniques have been increasingly used as a quantitative method to derive meaningful insights such as keywords [29], topics [30], and sentiment [31] from a set of textual data (e.g., transcripts) obtained from the interview. Previous studies have demonstrated the efficacy and potential of applying NLP techniques, addressing limitations (e.g., time-consuming, subjective, and error-prone) that reside in qualitative approaches such as interviews and surveys.…”