A Survey on Speech Large Language Models for Understanding
Abstract: Speech understanding is essential for interpreting the diverse forms of information embedded in spoken language, including linguistic, paralinguistic, and non-linguistic cues that are vital for effective human-computer interaction. The rapid advancement of LLMs has catalyzed the emergence of Speech LLMs (Speech LLMs), which marks a transformative shift toward general-purpose speech understanding systems. To further clarify and systematically delineate task objectives, in this paper, we formally define the concept of speech understanding and introduce a structured taxonomy encompassing its informational, functional, and format dimensions. Within this scope of definition, we present a comprehensive review of current Speech LLMs, analyzing their architectures through a three-stage abstraction: Modality Feature Extraction, Modality Information Fusion, and LLM Inference. In addition, we examine training strategies, discuss representative datasets, and review evaluation methodologies adopted in the field. Based on empirical analyses and experimental evidence, we identify two key challenges currently facing Speech LLMs: instruction sensitivity and degradation in semantic reasoning and propose concrete directions for addressing these issues. Through this systematic and detailed survey, we aim to offer a foundational reference for researchers and practitioners working toward more robust, generalizable, and human-aligned Speech LLMs.
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Summary
- The paper presents a comprehensive taxonomy of speech understanding tasks and architectures, detailing the evolution from modular to end-to-end frameworks.
- It outlines a three-stage model structure—from modality feature extraction to LLM inference—illustrating integration techniques and design considerations.
- It identifies key challenges such as instruction sensitivity and limited semantic reasoning, proposing future directions to enhance model robustness.
A Survey on Speech LLMs for Understanding
The paper "A Survey on Speech LLMs for Understanding" (2410.18908) presents a comprehensive examination of Speech LLMs (Speech LLMs) as transformative systems for speech understanding. This survey systematically defines speech understanding, explores the architectural evolutions, analyzes current methodologies, and highlights challenges along with potential directions for future advancements in Speech LLMs.
Definition and Taxonomy of Speech Understanding
The authors propose an inclusive perspective of speech understanding as the integrated process for interpreting spoken language through various dimensions: linguistic, paralinguistic, and non-linguistic information. Unlike traditional NLU that deals solely with textual inputs, speech understanding encompasses multimodal interaction, requiring models to perceive acoustic signals beyond textual content.
Figure 1: A Three-Dimensional Taxonomy of Speech Understanding Tasks.
The taxonomy devised by the authors organizes speech understanding tasks into informational, functional, and format dimensions, each providing insights into task objectives and system design strategies.
Evolution and Architectural Development
The paper delineates the historical progression of speech systems from modular architectures to end-to-end frameworks. Traditional cascaded pipelines were initially prevalent, with components for ASR and SLU being developed independently, often resulting in error propagation and inefficiencies.
Figure 2: The structural evolution of Speech LLMs, illustrating the transition from modular architectures to end-to-end frameworks.
Recent advancements have seen the rise of End-to-End (E2E) systems which integrate recognition and understanding into a coherent framework, promoting robustness and architectural simplicity. However, Speech LLMs represent a paradigm shift towards LLM-centric systems that leverage pretrained text models for reasoning directly from speech, marking a significant improvement in task generalization and holistic speech understanding.
Current Model Structures
The survey highlights the three-stage architecture prevalent in Speech LLMs today: Modality Feature Extraction, Modality Information Fusion, and LLM Inference. Feature extraction involves processing speech using self-supervised encoders like Whisper and Conformer, while fusion strategies align speech with text modalities through learned projections or token blending techniques.
Figure 3: Overview of Speech LLM Architectures with Speech and Text Inputs and Text Outputs.
While discrete tokenization of speech into text-like sequences provides format compatibility with LLM frameworks, continuous embeddings maintain richer acoustic information. Differences in representation reflect fundamental design choices and influence deployment scenarios.
Challenges and Future Directions
The paper identifies specific challenges faced by current Speech LLMs:
- Instruction Sensitivity: Models exhibit variability across different instruction formats, impacting reliability in real-world applications. Addressing this requires improved robustness and generalization strategies.
- Semantic Reasoning Limitation: While Speech LLMs align speech with textual outputs effectively, there remains a degradation in deeper semantic reasoning abilities. This poses limitations for tasks necessitating complex inference and discourse-level understanding.
Future exploration may focus on augmenting acoustic cue extraction, refining preference alignment methods like RLHF, and ensuring multitask adaptability and semantic consistency across diverse scenarios. These directions aim to enhance the interactivity, intelligence, and applicability of Speech LLMs.
Conclusion
Overall, the paper serves as a detailed reference on Speech LLMs, offering in-depth analysis of their progression, architecture, practicality, and challenges. It provides an overview of current capabilities while positioning Speech LLMs as pivotal for advancing speech processing towards more human-aligned, robust systems capable of understanding and interacting through spoken language. The insights presented underscore the importance of addressing existing limitations and pave the way for future innovations in Speech LLMs.
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