#speech

Two techniques for incorporating prosody into end-to-end SLU: prosody-attention and prosody-distillation. Up to 8% intent classification accuracy improvement on SLURP.

Map-Mix: a data augmentation approach using model training dynamics to guide latent mixup sampling, giving ~2% weighted F1 improvement on low-resource dialect classification.

The first public Indian-accented SLU dataset in the banking domain. SSL speech representations beat ASR-based approaches for intent classification.

Notes from a seminar covering six papers on code-mixing across NLP, speech synthesis, and speech recognition — including multilingual synthesis and code-mixed ASR.