A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.
Speech Model Pre-training for End-to-End Spoken Language Understanding
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Whereas conventional spoken language understanding (SLU) systems map speech to text, and then text to intent, end-to-end SLU systems map speech directly to intent through a single trainable model. Achieving high accuracy with these end-to-end models without a large amount of training data is difficult. We propose a method to reduce the data requirements of end-to-end SLU in which the model is first pre-trained to predict words and phonemes, thus learning good features for SLU. We introduce a new SLU dataset, Fluent Speech Commands, and show that our method improves performance both when the full dataset is used for training and when only a small subset is used. We also describe preliminary experiments to gauge the model's ability to generalize to new phrases not heard during training.
years
2026 2representative citing papers
WQ-Fusion combines Whisper and Qwen encoders with gated attention to reach 0.836 on the Interspeech 2026 Audio Encoder Capability Challenge, outperforming single-encoder baselines.
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.
-
WQ-Fusion: Dynamic Gated Attention for Cross-Domain Audio Representation
WQ-Fusion combines Whisper and Qwen encoders with gated attention to reach 0.836 on the Interspeech 2026 Audio Encoder Capability Challenge, outperforming single-encoder baselines.