A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding

• URL: http://arxiv.org/abs/2406.12141v1
• Date: Mon, 17 Jun 2024 23:07:53 GMT
• Title: A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding
• Authors: Gaëlle Laperrière, Sahar Ghannay, Bassam Jabaian, Yannick Estève,
• Abstract summary: Self-Supervised Learning is vastly used to efficiently represent speech for Spoken Language Understanding. textual SSL models are proposed to encode language-agnostic semantics. SAMU-XLSR framework employed this semantic information to enrich multilingual speech representations.
• Score: 12.887586659035497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-Supervised Learning is vastly used to efficiently represent speech for Spoken Language Understanding, gradually replacing conventional approaches. Meanwhile, textual SSL models are proposed to encode language-agnostic semantics. SAMU-XLSR framework employed this semantic information to enrich multilingual speech representations. A recent study investigated SAMU-XLSR in-domain semantic enrichment by specializing it on downstream transcriptions, leading to state-of-the-art results on a challenging SLU task. This study's interest lies in the loss of multilingual performances and lack of specific-semantics training induced by such specialization in close languages without any SLU implication. We also consider SAMU-XLSR's loss of initial cross-lingual abilities due to a separate SLU fine-tuning. Therefore, this paper proposes a dual task learning approach to improve SAMU-XLSR semantic enrichment while considering distant languages for multilingual and language portability experiments.

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