Cross-lingual and Multilingual Speech Emotion Recognition on English and French
This work addresses multilingual emotion recognition for low-resource languages, but it is incremental as it builds on existing cross-lingual models and focuses on specific language pairs.
The paper tackled the problem of inconsistent speech corpora in multilingual speech emotion recognition by building a system for English and French with similar interaction characteristics, and explored fine-tuning a pre-trained model with few target-language samples for low-resource applications.
Research on multilingual speech emotion recognition faces the problem that most available speech corpora differ from each other in important ways, such as annotation methods or interaction scenarios. These inconsistencies complicate building a multilingual system. We present results for cross-lingual and multilingual emotion recognition on English and French speech data with similar characteristics in terms of interaction (human-human conversations). Further, we explore the possibility of fine-tuning a pre-trained cross-lingual model with only a small number of samples from the target language, which is of great interest for low-resource languages. To gain more insights in what is learned by the deployed convolutional neural network, we perform an analysis on the attention mechanism inside the network.