Mayank Sharma

Prime Video developed a language-agnostic system to flag and automatically synchronize out-of-sync subtitles.

In this work, we present a Multi-Lingual (MLi) and Multi-Task Learning (MTL) audio only SER system based on the multi-lingual pre-trained wav2vec 2.0 model.

We show that, (a) audio based approach results in superior performance compared to other baselines, (b) benefit due to audio model is more pronounced on global multi-lingual data compared to English data and (c) the multi-modal model results in 63% rating accuracy and provides the ability to backfill top 90% Stream Weighted Coverage titles in PV catalog with 88% coverage at 91% accuracy.

This paper discusses the problem of missing transcription, where the subtitle blocks corresponding to some speech segments in the DEC are non-existent. We present a solution to augment human correction process by automatically identifying the timings associated with the non-transcribed dialogues in a language agnostic manner.

In this work, we present a thorough survey on DNN based VADs on DEC data in terms of their accuracy, Area Under Curve (AUC), noise sensitivity, and language agnostic behavior.