BeAts: Bengali Speech Acts Recognition using Multimodal Attention Fusion
This work addresses speech understanding in a low-resource language, Bengali, which is incremental as it applies existing multimodal techniques to a new domain.
The paper tackles speech acts recognition in Bengali by developing a multimodal model combining wav2vec2.0 for audio and MarianMT for text translation with attention fusion, achieving significant performance improvements over unimodal and simpler bimodal baselines.
Spoken languages often utilise intonation, rhythm, intensity, and structure, to communicate intention, which can be interpreted differently depending on the rhythm of speech of their utterance. These speech acts provide the foundation of communication and are unique in expression to the language. Recent advancements in attention-based models, demonstrating their ability to learn powerful representations from multilingual datasets, have performed well in speech tasks and are ideal to model specific tasks in low resource languages. Here, we develop a novel multimodal approach combining two models, wav2vec2.0 for audio and MarianMT for text translation, by using multimodal attention fusion to predict speech acts in our prepared Bengali speech corpus. We also show that our model BeAts ($\underline{\textbf{Be}}$ngali speech acts recognition using Multimodal $\underline{\textbf{At}}$tention Fu$\underline{\textbf{s}}$ion) significantly outperforms both the unimodal baseline using only speech data and a simpler bimodal fusion using both speech and text data. Project page: https://soumitri2001.github.io/BeAts