Papers by Sravya Popuri

5 papers
SpiRit-LM: Interleaved Spoken and Written Language Model (2025.tacl-1)

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Challenge: SpiRit-LM is a foundation multimodal language model that freely mixes text and speech.
Approach: They propose a multimodal language model that freely mixes text and speech . they extend the model to the speech modality by continuously training it on text and language units.
Outcome: The proposed model can learn new tasks in a few-shot fashion across modalities.
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)

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Challenge: a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data.
Approach: They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language .
Outcome: The proposed system can be used to train models in languages without standard writing systems.
UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units (2023.acl-long)

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Challenge: Experimental evaluations show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up.
Approach: They propose a two-pass direct S2ST architecture which generates textual representations and predicts discrete acoustic units . they show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up.
Outcome: The proposed architecture outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up on large datasets.
Textless Speech-to-Speech Translation on Real Data (2022.naacl-main)

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Challenge: Existing text-based speech-to-speech translation systems rely on cascaded approach . text-to text translation systems require text generation and a single input to generate output .
Approach: They propose a textless speech-to-speech translation system that can translate speech from one language into another without the need of text data.
Outcome: The proposed system can translate speech from one language into another without text data.
Direct Speech-to-Speech Translation With Discrete Units (2022.acl-long)

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Challenge: Existing direct speech-to-speech translation models rely on text generation as an intermediate step.
Approach: They propose a direct speech-to-speech translation model that translates speech from one language to another without relying on intermediate text generation.
Outcome: The proposed model produces 6.7 BLEUs in the Fisher Spanish-English dataset when trained without any text transcripts and with text supervision.

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