Spoken Language Understanding
114 papers with code • 5 benchmarks • 13 datasets
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Privacy-Preserving End-to-End Spoken Language Understanding
Thus, the SLU system needs to ensure that a potential malicious attacker cannot deduce the sensitive attributes of the users, while it should avoid greatly compromising the SLU accuracy.
What has LeBenchmark Learnt about French Syntax?
They are trained on very low level information (the raw speech signal), and do not have explicit lexical knowledge.
Evaluating and Improving Continual Learning in Spoken Language Understanding
In this work, we propose an evaluation methodology that provides a unified evaluation on stability, plasticity, and generalizability in continual learning.
The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese
In the field of spoken language understanding, systems like Whisper and Multilingual Massive Speech (MMS) have shown state-of-the-art performances.
Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model
Recent advances in self-supervised speech models have shown significant improvement in many downstream tasks.
Learning Semantic Information from Raw Audio Signal Using Both Contextual and Phonetic Representations
We propose a framework to learn semantics from raw audio signals using two types of representations, encoding contextual and phonetic information respectively.
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text.
Compositional Generalization in Spoken Language Understanding
State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack of model compositionality when trained on limited training data.
Generative Context-aware Fine-tuning of Self-supervised Speech Models
Considering the recent advances in generative large language models (LLM), we hypothesize that an LLM could generate useful context information using the preceding text.
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings
We build on existing light models for intent classification in Flemish, and our main contribution is applying different augmentation techniques on two levels -- the voice level, and the phonetic transcripts level -- to the existing models to counter the problem of scarce labeled data in low-resource languages.