Papers Spoken Language Understanding
“Spoken Language Understanding” 태그가 달린 논문 570편 · 필터 해제
SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding
Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decoding stochasticity, particularly in multi-intent scenarios. In view of…
Spoken Language UnderstandingSelective Capability Unlearning in End-to-End Spoken Language Understanding
Modern spoken language understanding (SLU) systems are increasingly deployed in real-world settings, where specific functionalities may need to be removed due to policy or safety constraints. In SLU, a functionality corr…
Spoken Language UnderstandingEnd-to-End Voice Intent Recognition for Spontaneous Human-Drone Interaction with Naive Users
Voice control offers an intuitive alternative to manual drone piloting, yet most existing systems rely on rigid command vocabularies that fail to handle the spontaneous, disfluent speech of naive users. This paper addres…
Spoken Language UnderstandingSelf-Supervised LearningKnowledge DistillationIntent RecognitionLaSR: Context-Aware Speech Recognition via Latent Reasoning
Recent advances in Speech Large Language Models (Speech LLMs) have significantly enhanced spoken language understanding and reasoning. However, their contextual awareness is limited, struggling to perform speech recognit…
Spoken Language UnderstandingSpeech RecognitionBangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization
Automatic Speech Recognition (ASR) and speaker diarization in Bangla remain challenging due to long form recordings, diverse acoustic conditions, and significant speaker variability. This work addresses these two core ta…
Spoken Language UnderstandingSpeaker DiarizationSpeech RecognitionData AugmentationSLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding
Spoken Language Understanding (SLU) aims to extract the semantic information from the speech utterance of user queries. It is a core component in a task-oriented dialogue system. With the spectacular progress of deep neu…
Spoken Language UnderstandingSpeech RecognitionA Generative Model for Joint Multiple Intent Detection and Slot Filling
In task-oriented dialogue systems, spoken language understanding (SLU) is a critical component, which consists of two sub-tasks, intent detection and slot filling. Most existing methods focus on the single-intent SLU, wh…
Task-Oriented Dialogue SystemsSpoken Language UnderstandingMulti-Task LearningIntent DetectionMulti-Intent Spoken Language Understanding: Methods, Trends, and Challenges
Multi-intent spoken language understanding (SLU) involves two tasks: multiple intent detection and slot filling, which jointly handle utterances containing more than one intent. Owing to this characteristic, which closel…
Spoken Language UnderstandingIntent DetectionSlot FillingMAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark
Spoken Language Understanding (SLU), which aims to extract user semantics to execute downstream tasks, is a crucial component of task-oriented dialog systems. Existing SLU datasets generally lack sufficient diversity and…
Spoken Language UnderstandingSpeech RecognitionIntroducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding
Spoken Language Understanding (SLU) consists of two sub-tasks: intent detection (ID) and slot filling (SF). Given its broad range of real-world applications, enhancing SLU for practical deployment is increasingly critica…
Spoken Language UnderstandingIntent DetectionKnowledge GraphsSlot FillingEchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models
Speech Language Models (SLMs) have made significant progress in spoken language understanding. Yet it remains unclear whether they can fully perceive non lexical vocal cues alongside spoken words, and respond with empath…
Spoken Language UnderstandingResponse GenerationM3-SLU: Evaluating Speaker-Attributed Reasoning in Multimodal Large Language Models
We present M3-SLU, a new multimodal large language model (MLLM) benchmark for evaluating multi-speaker, multi-turn spoken language understanding. While recent models show strong performance in speech and text comprehensi…
Spoken Language UnderstandingQuestion AnsweringProbing the Hidden Talent of ASR Foundation Models for L2 English Oral Assessment
In this paper, we explore the untapped potential of Whisper, a well-established automatic speech recognition (ASR) foundation model, in the context of L2 spoken language assessment (SLA). Unlike prior studies that extrin…
Spoken Language UnderstandingSpeech RecognitionSpeechLLMs for Large-scale Contextualized Zero-shot Slot Filling
Slot filling is a crucial subtask in spoken language understanding (SLU), traditionally implemented as a cascade of speech recognition followed by one or more natural language understanding (NLU) components. The recent a…
Natural Language UnderstandingSpoken Language UnderstandingZero-shot Slot FillingSpeech RecognitionExploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech Data
Large Audio Language Models (LALMs) have emerged as powerful tools for speech-related tasks but remain underexplored for fine-tuning, especially with limited speech data. To bridge this gap, we systematically examine how…
Spoken Language UnderstandingAFD-SLU: Adaptive Feature Distillation for Spoken Language Understanding
Spoken Language Understanding (SLU) is a core component of conversational systems, enabling machines to interpret user utterances. Despite its importance, developing effective SLU systems remains challenging due to the s…
Spoken Language UnderstandingSpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings
While integrating speech encoder with LLM requires substantial data and resources, use cases face limitations due to insufficient availability. To address this, we propose a solution with a parameter-efficient adapter th…
Spoken Language UnderstandingSpeech RecognitionSentiment AnalysisSpeech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs
With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has demonstrated strong capabilities in audio…
Spoken Language UnderstandingSelf-Supervised LearningWhat do Speech Foundation Models Learn? Analysis and Applications
Speech foundation models (SFMs) are designed to serve as general-purpose representations for a wide range of speech-processing tasks. The last five years have seen an influx of increasingly successful self-supervised and…
Spoken Language UnderstandingSpeech RecognitionUniSLU: Unified Spoken Language Understanding from Heterogeneous Cross-Task Datasets
Spoken Language Understanding (SLU) plays a crucial role in speech-centric multimedia applications, enabling machines to comprehend spoken language in scenarios such as meetings, interviews, and customer service interact…
Spoken Language UnderstandingSpeech RecognitionSentiment Analysis