  Recently, researchers have utilized neural network-based speaker embedding
techniques in speaker-recognition tasks to identify speakers accurately.
However, speaker-discriminative embeddings do not always represent speech
features such as age group well. In an embedding model that has been highly
trained to capture speaker traits, the task of age group classification is
closer to speech information leakage. Hence, to improve age group
classification performance, we consider the use of speaker-discriminative
embeddings derived from adversarial multi-task learning to align features and
reduce the domain discrepancy in age subgroups. In addition, we investigated
different types of speaker embeddings to learn and generalize the
domain-invariant representations for age groups. Experimental results on the
VoxCeleb Enrichment dataset verify the effectiveness of our proposed adaptive
adversarial network in multi-objective scenarios and leveraging speaker
embeddings for the domain adaptation task.
