  Over the past few years, self-supervised learned speech representations have
emerged as fruitful replacements for conventional surface representations when
solving Spoken Language Understanding (SLU) tasks. Simultaneously, multilingual
models trained on massive textual data were introduced to encode language
agnostic semantics. Recently, the SAMU-XLSR approach introduced a way to make
profit from such textual models to enrich multilingual speech representations
with language agnostic semantics. By aiming for better semantic extraction on a
challenging Spoken Language Understanding task and in consideration with
computation costs, this study investigates a specific in-domain semantic
enrichment of the SAMU-XLSR model by specializing it on a small amount of
transcribed data from the downstream task. In addition, we show the benefits of
the use of same-domain French and Italian benchmarks for low-resource language
portability and explore cross-domain capacities of the enriched SAMU-XLSR.
