Papers by Srikanth Ronanki
In Other News: a Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data (N19-2)
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Nishant Prateek, Mateusz Łajszczak, Roberto Barra-Chicote, Thomas Drugman, Jaime Lorenzo-Trueba, Thomas Merritt, Srikanth Ronanki, Trevor Wood
| Challenge: | Recent advances in text-to-speech synthesis have enabled researchers to generate high-quality speech with a wide range of prosodic variations. |
| Approach: | They propose a model that can synthesise newscaster-style speech with a few hours of data . they propose to factor in contextual word embeddings and evaluate it against neutral synthesis . |
| Outcome: | The proposed model can synthesise newscaster-style speech with just a few hours of data. |
AdaBERT-CTC: Leveraging BERT-CTC for Text-Only Domain Adaptation in ASR (2023.emnlp-industry)
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| Challenge: | End-to-end (E2E) automatic speech recognition models struggle to recognize out-of-domain words such as proper nouns and domain-specific terms. |
| Approach: | They propose a domain adaptation technique that relies solely on textual data to adapt to out-of-domain words. |
| Outcome: | The proposed method outperforms the base model by up to 14% relative word error rate improvement on several out-of-domain, publicly available datasets. |
Context Length Alone Hurts LLM Performance Despite Perfect Retrieval (2025.findings-emnlp)
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Yufeng Du, Minyang Tian, Srikanth Ronanki, Subendhu Rongali, Sravan Babu Bodapati, Aram Galstyan, Azton Wells, Roy Schwartz, Eliu A Huerta, Hao Peng
| Challenge: | Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support. |
| Approach: | They propose a model-agnostic mitigation strategy that transforms a long-context task into a short-concept one by prompting the model to recite the retrieved evidence before attempting to solve the problem. |
| Outcome: | The proposed model improves on a long-context task up to 4% on RULER. |
LAWCAT: Efficient Distillation from Quadratic to Linear Attention with Convolution across Tokens for Long Context Modeling (2025.findings-emnlp)
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Zeyu Liu, Souvik Kundu, Lianghao Jiang, Anni Li, Srikanth Ronanki, Sravan Babu Bodapati, Gourav Datta, Peter Anthony Beerel
| Challenge: | a novel linearization framework is proposed to reduce the cost of training transformers from scratch. |
| Approach: | They propose a linear attention framework that integrates pre-trained transformers into a performant linear attention architecture. |
| Outcome: | The proposed framework improves performance on mistral-7B with 1K-length sequences and BABILong benchmarks. |
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs (2023.emnlp-industry)
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| Challenge: | End-to-end ASR models struggle to recognize uncommon domain-specific words due to limited audio context. |
| Approach: | They propose a "Retrieve and Copy" mechanism to improve latency while retaining the accuracy even when scaled to a large catalog. |
| Outcome: | The proposed method achieves 6% more word error rate reduction and 3.6% improvement in F1 when scaled to a large catalog size while retaining the accuracy. |
SpeechGuard: Exploring the Adversarial Robustness of Multi-modal Large Language Models (2024.findings-acl)
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Raghuveer Peri, Sai Muralidhar Jayanthi, Srikanth Ronanki, Anshu Bhatia, Karel Mundnich, Saket Dingliwal, Nilaksh Das, Zejiang Hou, Goeric Huybrechts, Srikanth Vishnubhotla, Daniel Garcia-Romero, Sundararajan Srinivasan, Kyu Han, Katrin Kirchhoff
| Challenge: | Integrated Speech and Large Language Models (SLMs) that follow speech instructions and generate relevant text responses have gained popularity lately. |
| Approach: | They propose algorithms that can generate adversarial examples to jailbreak SLMs without human involvement. |
| Outcome: | The proposed algorithms achieve state-of-the-art on spoken question-answering task scoring over 80% on both safety and helpfulness metrics. |