arxiv_cl 90% Match Research Paper AI researchers,Speech technologists,NLP engineers,Developers of voice assistants 2 weeks ago

Slot Filling as a Reasoning Task for SpeechLLMs

large-language-models › reasoning

📄 Abstract

Abstract: We propose integration of reasoning into speech large language models (speechLLMs) for the end-to-end slot-filling task. Inspired by the recent development of reasoning LLMs, we use a chain-of-thought framework to decompose the slot-filling task into multiple reasoning steps, create a reasoning dataset and apply the supervised fine-tuning strategy to a speechLLM. We distinguish between regular and reasoning speechLLMs and experiment with different types and sizes of LLMs as their text foundation models. We demonstrate performance improvements by introducing reasoning (intermediate) steps. However, we show that a reasoning textual LLM developed mainly for math, logic and coding domains might be inferior as a foundation model for a reasoning speechLLM. We further show that hybrid speechLLMs, built on a hybrid text foundation LLM and fine-tuned to preserve both direct and reasoning modes of operation, have better performance than those fine-tuned employing only one mode of operation.

Authors (3)

Kadri Hacioglu

Manjunath K E

Andreas Stolcke

Submitted

October 22, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

Proposes integrating chain-of-thought reasoning into speech LLMs for the slot-filling task, demonstrating performance improvements by decomposing the task into reasoning steps. It also investigates the impact of different text foundation models and introduces hybrid speechLLMs for better performance.

Business Value

Enables more sophisticated and accurate voice assistants and conversational agents capable of complex information extraction and task completion, improving user experience in applications like customer service and personal assistants.

Paper Metadata

Innovation Type

Algorithmic Integration

Deployment Feasibility

Moderate. Requires integration of speech recognition, LLM reasoning capabilities, and fine-tuning. The choice of foundation model is critical.

Limitations Addressed

Limited reasoning capabilities in traditional speech systems,Potential inferiority of text-based reasoning models (trained for math/logic) as foundations for speechLLMs,Need for models that can handle both direct and reasoning-based operations

Performance Gains

Demonstrates performance improvements by introducing reasoning steps; hybrid models show better performance than single-mode fine-tuned models.

Technical Tags

slot fillingspeechLLMsreasoningchain-of-thoughtsupervised fine-tuningfoundation modelsmultimodal AIend-to-end systems

Research Topics

Speech ProcessingNatural Language ProcessingMachine LearningArtificial IntelligenceReasoning in AI

Methods & Architectures

Chain-of-Thought (CoT) reasoningSupervised Fine-Tuning (SFT)Hybrid Foundation ModelsReasoning Dataset Creation SpeechLLMsText Foundation Models (LLMs)

Applications & Tasks

Conversational AI Speech Recognition Human-Computer Interaction Information ExtractionTask CompletionReasoning Under Uncertainty End-to-end slot filling using speech inputIntegrating reasoning capabilities into speech-based LLMs

Related Fields

Speech TechnologyConversational AICognitive ScienceMachine Learning

Keywords

SpeechLLMSlot FillingReasoningChain-of-ThoughtSupervised Fine-TuningFoundation ModelsMultimodal AIConversational AIVoice AssistantsInformation Extraction

Academic Context

#Speech Processing#Natural Language Processing#Machine Learning#Artificial Intelligence#Reasoning in AI

Commercial Potential

Potential Products

Advanced voice assistantsIntelligent call center automation systemsSpeech-based data entry and analysis tools

Target Industries

TechnologyCustomer ServiceTelecommunicationsAutomotive

Use Case Examples

A voice assistant that can understand complex booking requests involving multiple details (e.g., flight, hotel, car)Automated systems for transcribing and extracting information from customer service callsVoice-controlled interfaces for complex software applications

Competitive Edge

Extends reasoning capabilities, previously explored in text-only LLMs, to the speech domain, offering a more advanced approach to tasks like slot filling compared to traditional methods.

Market Opportunity

Growing market for voice-enabled AI and conversational interfaces.

Revenue Models

Licensing of speech AI technologyintegration into SaaS platforms.

Resource Requirements

Compute Needs

High (for training/fine-tuning speechLLMs and large foundation models)

Data Requirements

Requires a dataset suitable for slot filling, potentially annotated with reasoning steps, and speech data paired with text transcripts.

Deployment Constraints

Latency in speech processing and reasoning, computational cost, accuracy of speech recognition.

Scalability

Scalability depends on the underlying LLM and speech processing infrastructure.

Regulatory Considerations

Data privacy for voice recordings.

Production Readiness

Maturity Level

Research/Development

Time to Market

2-3 years for robust commercial applications

Patent Potential

Moderate (integration of reasoning into speechLLMs)

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