Papers with speech recognition model

2 papers
Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers (2023.emnlp-main)

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Challenge: 'context mixing' is a feature of Transformers that is used to build up representations of acoustic and linguistic structure in speech models.
Approach: They propose to use a French spelling quirk to probe context mixing in speech models to find out how to translate spoken words into written equivalents.
Outcome: The proposed model incorporates cues to identify correct transcription, whereas encoder-decoder models relegate task to decoder modules.
Harnessing Whisper for Prosodic Stress Analysis (2025.findings-acl)

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Challenge: Prosody affects how people produce and understand language, yet studies of how it does so have been hindered by the lack of efficient tools for analyzing prosodic stress.
Approach: They propose to use OpenAI Whisper large-v2 to analyze prosodic stress . they propose to characterize decision boundaries used by Whisper for prosodic stress interpretation .
Outcome: The proposed model can learn gender-specific stress patterns and transfer learning from one type of stress to another, surpassing traditional machine learning models.

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