Papers by Ambuj Mehrish
HYPERTTS: Parameter Efficient Adaptation in Text to Speech Using Hypernetworks (2024.lrec-main)
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| Challenge: | Neural text-to-speech (TTS) systems limited to predefined speaker styles or specific sets of speaker IDs. |
| Approach: | They propose a network that can adapt adapter parameters to new speakers . they compare two domain adaptation settings and find it to be very efficient . |
| Outcome: | The proposed Adapters improve speech synthesis performance on two domains and compare them with baselines. |
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models (2024.findings-naacl)
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| Challenge: | Neural Text-to-Speech systems are a promising approach for high-fidelity speech synthesis . but the efficiency of multi-step sampling in Diffusion Models presents challenges . |
| Approach: | They propose a novel architecture grounded in consistency models to improve model convergence. |
| Outcome: | The proposed architecture achieves top-quality speech synthesis in fewer steps without adversarial training or pre-trained model dependencies. |
Reward-Guided Tree Search for Inference Time Alignment of Large Language Models (2025.naacl-long)
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| Challenge: | Inference-time computation methods enhance performance of Large Language Models by leveraging additional computational resources. |
| Approach: | They propose an inference-time alignment method that leverages a reward model to achieve alignment through reward-guided tree search. |
| Outcome: | The proposed method outperforms other inference-time alignment methods on two benchmarks . it achieves comparable performance to preference-tuned models on both benchmarks, authors show . |