  We recently developed SLM, a joint speech and language model, which fuses a
pretrained foundational speech model and a large language model (LLM), while
preserving the in-context learning capability intrinsic to the pretrained LLM.
In this paper, we apply SLM to speech dialog applications where the dialog
states are inferred directly from the audio signal.
  Task-oriented dialogs often contain domain-specific entities, i.e.,
restaurants, hotels, train stations, and city names, which are difficult to
recognize, however, critical for the downstream applications. Inspired by the
RAG (retrieval-augmented generation) paradigm, we propose a retrieval augmented
SLM (ReSLM) that overcomes this weakness. We first train a speech retriever to
retrieve text entities mentioned in the audio. The retrieved entities are then
added as text inputs to the underlying SLM to bias model predictions. We
evaluated ReSLM on speech MultiWoz task (DSTC-11 challenge), and found that
this retrieval augmentation boosts model performance, achieving joint goal
accuracy (38.6% vs 32.7%), slot error rate (20.6% vs 24.8%) and ASR word error
rate (5.5% vs 6.7%). While demonstrated on dialog state tracking, our approach
is broadly applicable to other speech tasks requiring contextual information or
domain-specific entities, such as contextual ASR with biasing capability.
