Oct 14, 2026
Zero-Shot Spoken Language Understanding with SLAM-ASR: What Do Monolingual ASR-Trained Linear Projectors Enable?
The SLAM-ASR framework connects the features of a frozen speech encoder as input to a frozen LLM decoder to enable speech transcription. In this work we ask whether this training-on speech transcription-provides the LLM with the capability to also perform spoken language understanding (SLU) tasks, by training monolingual projectors on 11 languages
Authors
Edwin Arkel Rios, Sneh Sourabh, Jocelyn Zaruma, Girish Ewoorkar, Julian Mack, Bo-Cheng Lai
Abstract
The SLAM-ASR framework connects the features of a frozen speech encoder as input to a frozen LLM decoder to enable speech transcription. In this work we ask whether this training-on speech transcription-provides the LLM with the capability to also perform spoken language understanding (SLU) tasks, by training monolingual projectors on 11 languages. When prompted to answer a yes/no question about the input speech, the mean instruction following rate (IFR) is 49.8%. When not following the instruction, these models default back to transcription. The transcription quality-measured by the character error rate (CER)-is not sufficient to predict IFR. Remarkably, when the model is instructed to answer the topic question as a single digit option, the mean IFR rises to 81.3%. Our models score a mean 23.2% topic balanced accuracy (BAcc) against 14.3% chance. However, the same decoder scores a mean 71.5% BAcc when using reference transcripts as text input. This indicates a failure of the projector to transfer information for the SLU tasks.
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