Part 8/13:

• Emotion and Tone Recognition: Enhancing models to understand and generate nuanced emotions, such as sarcasm or excitement.

• Multi-Participant and Noisy Environments: Improving audio understanding in real-world settings with overlapping speakers or background noise.

• Dialect and Regional Variations: Addressing speech variations within the same language, like Hindi in Bihar versus Delhi, through Residual Learning Frameworks (RLF).

• Cross-Lingual Transfer: Developing models that can transfer learning across languages, especially where data scarcity exists.

• Voice Cloning and Deepfakes: Moving beyond simple cloning to more sophisticated, emotionally expressive voice synthesis.