Layer-wise probing of wav2vec2-base and Whisper-small shows both models distinguish reduced vs. canonical consonant clusters in AAE with high accuracy and retain cues to underlying stops, encoding CCR as gradient variation.
Transformer Transducer: A Streamable Speech Recognition Model
5 Pith papers cite this work. Polarity classification is still indexing.
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MIRAGE uses adaptive multimodal gating on native multimodal backbones plus a transformer encoder to achieve state-of-the-art whole-brain fMRI prediction for naturalistic audiovisual stimuli, outperforming post-hoc unimodal aggregation.
An encoding probe reconstructs transformer representations from acoustic, phonetic, syntactic, lexical and speaker features, showing independent syntactic/lexical contributions and training-dependent speaker effects.
WhisperPipe delivers 89 ms median latency and 48% lower peak GPU memory than standard Whisper while keeping word error rate within 2% of the offline model.
MOSS-Audio is an audio-language model using a 12.5 Hz encoder, DeepStack cross-layer injection, time markers, and an event-preserving annotation pipeline for unified audio understanding.
citing papers explorer
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Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English
Layer-wise probing of wav2vec2-base and Whisper-small shows both models distinguish reduced vs. canonical consonant clusters in AAE with high accuracy and retain cues to underlying stops, encoding CCR as gradient variation.
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MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding
MIRAGE uses adaptive multimodal gating on native multimodal backbones plus a transformer encoder to achieve state-of-the-art whole-brain fMRI prediction for naturalistic audiovisual stimuli, outperforming post-hoc unimodal aggregation.
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Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe
An encoding probe reconstructs transformer representations from acoustic, phonetic, syntactic, lexical and speaker features, showing independent syntactic/lexical contributions and training-dependent speaker effects.
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WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition
WhisperPipe delivers 89 ms median latency and 48% lower peak GPU memory than standard Whisper while keeping word error rate within 2% of the offline model.
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MOSS-Audio Technical Report
MOSS-Audio is an audio-language model using a 12.5 Hz encoder, DeepStack cross-layer injection, time markers, and an event-preserving annotation pipeline for unified audio understanding.