  The assessment of children at risk of autism typically involves a clinician
observing, taking notes, and rating children's behaviors. A machine learning
model that can label adult and child audio may largely save labor in coding
children's behaviors, helping clinicians capture critical events and better
communicate 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 build
a unified system for tasks of clinician-child speaker diarization and
vocalization classification (VC). To enhance children's VC, we build a W2V2
phoneme recognition system for children under 4 years old, and we incorporate
its phonetically-tuned embeddings as auxiliary features or recognize pseudo
phonetic transcripts as an auxiliary task. We test our method on two corpora
(Rapid-ABC and BabbleCor) and obtain consistent improvements. Additionally, we
outperform the state-of-the-art performance on the reproducible subset of
BabbleCor. Code available at https://huggingface.co/lijialudew
