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The assessment of children at risk of autism typically involves a clinicianobserving, taking notes, and rating children's behaviors. A machine learningmodel that can label adult and child audio may largely save labor in codingchildren's behaviors, helping clinicians capture critical events and bettercommunicate with parents. In this study, we leverage Wav2Vec 2.0 (W2V2),pre-trained on 4300-hour of home audio of children under 5 years old, to builda unified system for tasks of clinician-child speaker diarization andvocalization classification (VC). To enhance children's VC, we build a W2V2phoneme recognition system for children under 4 years old, and we incorporateits phonetically-tuned embeddings as auxiliary features or recognize pseudophonetic transcripts as an auxiliary task. We test our method on two corpora(Rapid-ABC and BabbleCor) and obtain consistent improvements. Additionally, weoutperform the state-of-the-art performance on the reproducible subset ofBabbleCor. Code available at https://huggingface.co/lijialudew

Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis