Yogesh Virkar

Jun 25, 2024 | amazon.science | Yogesh Virkar |Prashant Mathur |Proyag Pal |Brian Thompson

To translate speech for automatic dubbing, machine translation needs to be isochronous, i.e. translated speech needs to be aligned with the source in terms of speech durations. We introduce target factors in a transformer model to predict durations jointly with target language phoneme sequences. We also introduce auxiliary counters to help the decoder to keep track of the timing information while generating target phonemes.

Nov 22, 2023 | amazon.science | Surafel Melaku Lakew |Yogesh Virkar |Prashant Mathur |Marcello Federico

Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech. For neural MT, generating translations of length close to the source length (e.g. within ±10% in character count), while preserving quality is a challenging task.

Mar 9, 2023 | amazon.science | Surafel Melaku Lakew |Yogesh Virkar |Prashant Mathur |Marcello Federico

How do you design and provide right incentives for millions of sellers that inbound and ship billions of customer orders? How do you measure sellers' response to /causal impacts of capacity control policies we implemented at Amazon using the state-of-the-art econometric techniques? How do you optimize Amazon’s third-party supply chain using new ideas never implemented at this scale to benefit millions of customers worldwide? How do you design and evaluate seller assistance to drive their success?

Mar 8, 2023 | amazon.science | William Brannon |Yogesh Virkar |Joaquín Cos Delgado |Brian Thompson

We investigate how humans perform the task of dubbing video content from one language into another, leveraging a novel corpus of 319.57 hours of video from 54 professionally produced titles. This is the first such largescale study we are aware of.

Feb 25, 2023 | arxiv.org | Prashant Mathur |Yogesh Virkar |Surafel M. Lakew

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