Apple ML Research tackles long-form audio decoding challenges

Apple Machine Learning Research has published a paper detailing Segmental Attention Decoding with Long Form Acoustic Encodings. This research addresses the limitations of attention-based encoder-decoder models when processing long audio segments by proposing four modifications. These include injecting positional encodings, using extended acoustic context for training, segment concatenation, and semantic segmentation to improve accuracy and enable auto-regressive decoding. AI

IMPACT Improves capabilities for processing long audio inputs in AI models.

RANK_REASON The cluster contains a research paper from a major tech company's ML research division. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Apple Machine Learning Research →

• Apple Machine Learning Research
• Dogan Can
• Mingbin Xu
• Pawel Swietojanski
• Segmental Attention Decoding with Long Form Acoustic Encodings
• Takaaki Hori
• Xiaodan Zhuang
• Xinwei Li

• paper
• model release

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