A comprehensive study of 17 Large Language Models as automated annotators for Bangla hate speech detection reveals significant bias and instability issues. The research found that larger models don't necessarily perform better than smaller, task-specific ones, raising concerns about LLM reliability for sensitive annotation tasks in low-resource languages.
Researchers developed a hybrid AI model combining BanglaBERT and stacked LSTM networks to detect multiple types of cyberbullying in Bangla text simultaneously. The approach addresses limitations in existing single-label classification methods by recognizing that comments can contain overlapping forms of abuse like threats, hate speech, and harassment.
Researchers developed a robust framework for Bangla automatic speech recognition and speaker diarization that can handle long-form audio exceeding 30-60 seconds. The system uses Voice Activity Detection optimization and Connectionist Temporal Classification segmentation to maintain accuracy over extended durations in multi-speaker environments.
