Papers by Paarth Neekhara
Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance (2025.emnlp-main)
Copied to clipboard
Shehzeen Samarah Hussain, Paarth Neekhara, Xuesong Yang, Edresson Casanova, Subhankar Ghosh, Roy Fejgin, Mikyas T. Desta, Rafael Valle, Jason Li
| Challenge: | Autoregressive speech token generation models suffer from hallucinations and undesired vocalizations that do not conform to conditioning inputs. |
| Approach: | They propose an encoder-decoder transformer model that improves contextual adherence of speech token generation LLMs through preference alignment and classifier-free guidance. |
| Outcome: | The proposed model outperforms previous LLM-based models on intelligibility, speaker similarity and naturalness. |
Adversarial Reprogramming of Text Classification Neural Networks (D19-1)
Copied to clipboard
| Challenge: | Recent studies have shown that adversarial examples can cause a machine learning model to misclassify a sample from the classifier's input domain. |
| Approach: | They propose a context-based vocabulary remapping method that performs a computationally inexpensive input transformation to reprogram a victim classification model for a new set of sequences. |
| Outcome: | The proposed method performs a cost-effective input transformation to reprogram a model for a new set of sequences without altering the network architecture or parameters. |