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A Study of Spoken Audio Processing using Machine Learning for Libraries, Archives and Museums (LAM)

As the need to provide access to spoken word audio collections in libraries, archives, and museums (LAM) increases, so does the need to process them efficiently and consistently. Traditionally, audio processing involves listening to the audio files, conducting manual transcription, and applying controlled subject terms to describe them. This workflow takes significant time with each recording. In this study, we investigate if and how machine learning (ML) can facilitate processing of audio collections in a manner that corresponds with LAM best practices. We use the StoryCorps collection of oral histories "Las Historias," and fixed subjects (metadata) that are manually assigned to describe each of them. Our methodology has two main phases. First, audio files are automatically transcribed using two automatic speech recognition (ASR) methods. Next, we build different supervised ML models for label prediction using the transcription data and the existing metadata. Throughout these phases the results are analyzed quantitatively and qualitatively. The workflow is implemented within the flexible web framework IDOLS to lower technical barriers for LAM professionals. By allowing users to submit ML jobs to supercomputers, reproduce workflows, change configurations, and view and provide feedback transparently, this workflow allows users to be in sync with LAM professional values. The study has several outcomes including a comparison of the quality between different transcription methods and the impact of that quality on label prediction accuracy. The study also unveiled the limitations of using manually assigned metadata to build models, to which we suggest alternate strategies for building successful training data.