A Multimodal Bangla Text–Audio Dataset for Sentiment Analysis

• Bangla, a language spoken by more than 230 million people worldwide, is significantly underrepresented in speech and sentiment analysis research when compared to high-resource languages.
• This is addressed with the dataset. Researchers and developers working on low-resource language technologies, such as sentiment analysis, speech recognition, and multimodal learning frameworks, should find this extensive resource very helpful.
• Sentiment-aware speech recognition, speech-based emotion detection, emotionally expressive text-to-speech systems, multimodal sentiment classification, and speaker-independent recognition models are just a few of the many applications that can be developed and evaluated using this dataset.
• Its modular structure promotes continuous research expansion by enabling contributors to add new regional vocabularies, dialectal variations, or additional sentiment classes over time.
• The dataset is precisely balanced, with 4,000 audio recordings created by four native speakers (two male and two female) and 500 samples for each sentiment category. The sentences capture the natural and everyday use of the Bangla language, spanning a wide range of topics that include events, emotions, personal experiences, and general statements.