  The prediction of valence from speech is an important, but challenging
problem. The externalization of valence in speech has speaker-dependent cues,
which contribute to performances that are often significantly lower than the
prediction of other emotional attributes such as arousal and dominance. A
practical approach to improve valence prediction from speech is to adapt the
models to the target speakers in the test set. Adapting a speech emotion
recognition (SER) system to a particular speaker is a hard problem, especially
with deep neural networks (DNNs), since it requires optimizing millions of
parameters. This study proposes an unsupervised approach to address this
problem by searching for speakers in the train set with similar acoustic
patterns as the speaker in the test set. Speech samples from the selected
speakers are used to create the adaptation set. This approach leverages
transfer learning using pre-trained models, which are adapted with these speech
samples. We propose three alternative adaptation strategies: unique speaker,
oversampling and weighting approaches. These methods differ on the use of the
adaptation set in the personalization of the valence models. The results
demonstrate that a valence prediction model can be efficiently personalized
with these unsupervised approaches, leading to relative improvements as high as
13.52%.

Sound;Computation and Language;Human-Computer Interaction;Audio and Speech Processing