Analyzing natural speech and language samples of children is a well-known source of insights when conducting research in the field of speech and language acquisition. The process of collecting, manual transcription and analysis of these data however is extremely time-consuming and costly. Because of that, the data basis for speech and language development research is scarce.
Meanwhile speech recognition and processing technology has been developed to a point where use for research purposes in linguistics and speech-language-pathology seems possible. For the recognition of adult language, technology has evolved to mainstream applications. However processing child utterances is much more challenging due to their acoustic and language properties.
The project is an interdisciplinary collaboration with the Department for Speech and Language Therapy of the Institute of Special Education (IFS). By combining the domain knowledge of the IFS about children's speech and our expertise in machine learning and signal processing we aim to improve automatic speech recognition of children's speech to the point were it can be used for applications in speech language therapy.
The project is part of the interdisciplinary collaboration "Leibniz Lab for Relational Communication Research" (Project Website).