Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier
This addresses the problem of processing naturalistic speech data from children for researchers in speech sciences, but it is incremental as it highlights obstacles rather than breakthroughs.
The paper tackled the challenge of improving Voice Type Classification for child-worn device recordings, finding that enhancements in features, architecture, and parameters led to only marginal performance gains, with more progress achieved by focusing on data relevance and quantity.
Recordings gathered with child-worn devices promised to revolutionize both fundamental and applied speech sciences by allowing the effortless capture of children's naturalistic speech environment and language production. This promise hinges on speech technologies that can transform the sheer mounds of data thus collected into usable information. This paper demonstrates several obstacles blocking progress by summarizing three years' worth of experiments aimed at improving one fundamental task: Voice Type Classification. Our experiments suggest that improvements in representation features, architecture, and parameter search contribute to only marginal gains in performance. More progress is made by focusing on data relevance and quantity, which highlights the importance of collecting data with appropriate permissions to allow sharing.