Stanford probes 24 speech encoders: Conformers and Transformers listen differently
stanfordnlp · x · 2026-10-23
Stanford NLP shared a new study probing 24 speech encoders, finding notable architectural differences: speech-sound categories are easiest to read out early in Conformers, but only much later in Transformers.
The work will be presented at EMNLP 2026 in the Monday 9:00–10:30 AM CET session, Room F9–10. The findings offer insight into internal representations of speech models and guidance for choosing encoders in speech applications.
More from Research
- VerifierBench: 222 Tasks Expose How Unreliable Agent-Generated Verifiers Are — Jianing Yin · 2026-10-23
- LM2LM: Frozen World Model Provides Scalable RL Rewards for AI Self-Improvement — Minhua Lin · 2026-10-23
- Stanford prof: LLM-fused models need radically new evals to separate memory from reasoning — anshulkundaje · 2026-10-23
- Stanford professor warns fused LLMs blur the line between memorization and real reasoning — anshulkundaje · 2026-10-23
- NSF picks nine teams for up to $3.5M each to boost super intelligence infrastructure efficiency — sh_reya · 2026-10-23
- CMU professor wins NSF-STRIDE AI Efficiency Challenge grant, hiring PhD students and postdocs — sh_reya · 2026-10-23