Open-Format Speech Understanding Tasks

• Open-format speech understanding tasks are defined as interpreting free-form speech to extract and generate semantic content without fixed label constraints.
• These tasks integrate ASR, spoken language understanding, sequence generation, and real-time dialogue processing for applications like intent detection and emotion recognition.
• Recent advances leverage modular design, multi-task learning, and robust benchmarking to address challenges in paralinguistic analysis and multi-turn context handling.

Open-format speech understanding tasks are defined by the requirement that models must process naturally occurring, unconstrained speech inputs and produce outputs that match the semantic intent or content, often without prior knowledge of strict label or structural constraints. These tasks extend far beyond traditional automatic speech recognition (ASR) to encompass complex spoken language understanding (SLU), sequence generation, real-time dialogue, and integration of paralinguistic and contextual information. Recent research has established empirically sound methodologies, benchmarks, and model architectures that collectively advance the development and evaluation of open-format speech understanding systems.

1. Defining Characteristics and Taxonomy

Open-format speech understanding tasks operate over diverse input and output spaces. Unlike “closed-format” recognition—where output is typically restricted to fixed vocabularies or class sets (e.g., digit recognition)—open-format SLU encompasses extraction or generation of content in free-form text, semantic parsing, slot/intent detection, and even higher-level reasoning across conversational turns. Core categories, as organized by benchmarks such as Dynamic-SUPERB Phase-2 (Huang et al., 8 Nov 2024), MMSU (Wang et al., 5 Jun 2025), and URO-Bench (Yan et al., 25 Feb 2025), include:

• Spoken Language Understanding: Named entity recognition (NER), intent detection, semantic parsing, sentiment/emotion recognition.
• Sequence Generation: Question answering, summarization, translation, grammar correction, dialogue continuation.
• Multi-modal and Paralinguistic Analysis: Speaker traits (gender, age), style, prosody, accent identification, vocal event detection.
• Reasoning and Real-World Tool Integration: Goal-driven task completion, context tracking in dialogues, integration with external APIs (calendar, web search).
• Low-level Perception: Phoneme classification, stress/intonation analysis, speech enhancement for robust understanding.

This broad taxonomy is formalized in Dynamic-SUPERB Phase-2’s hierarchical task map, which systematizes coverage across speech, music, environmental audio, and more, with explicit attention to regression, classification, and sequence generation modalities.

2. Architectural and Training Paradigms

Architectural advances underpinning open-format SLU emphasize modularity, scalability, and multi-task learning:

• Modularized Universal Models: SpeechNet (Chen et al., 2021) exemplifies a fully modular architecture, decomposing processing into prosody, speaker, and content encoders, and employing modality-specific decoders, with all tasks uniformly formulated as “speech/text in → speech/text out.” Joint parameter updates via accumulated gradients support multi-task generalization, and plug-and-play modules (Prosody Predictor, etc.) allow rapid inclusion of new components for novel task domains.
• Encoder-Decoder and Decoder-Only LLMs: Encoder-decoder models (e.g., UniverSLU (Arora et al., 2023), OWSM-CTC (Peng et al., 20 Feb 2024)) leverage strong pre-trained ASR backbones (such as Whisper), augmenting them via instruction or token-based prompts to unify multiple downstream tasks, while decoder-only models (VoxtLM (Maiti et al., 2023)) employ discrete speech tokenization and unified embedding spaces.
• LLM Integration and Adaptation Layers: WEST (Zhang et al., 24 Sep 2025) and OSUM (Geng et al., 23 Jan 2025) integrate speech encoders (Whisper, WeNet, Paraformer) with LLMs (Qwen2, LLaMA), utilizing adaptation layers (e.g., convolutional projectors, LoRA) to bridge modalities and support fine-grained adaptation via low-rank parameter updates.
• Multi-task and Reinforcement Learning: Multi-task strategies (OSUM’s ASR+X paradigm) and reinforcement learning frameworks (GRPO with BLEU reward (Elmakies et al., 21 Sep 2025)) enable models to simultaneously optimize for ASR and a spectrum of understanding tasks, with reinforcement-driven task-specific output quality improvements.

The prevalence of instruction-based prompting (Discriminative task specifiers in UniverSLU (Arora et al., 2023)) and prompt-encoded conditioning (e.g., in OWSM-CTC (Peng et al., 20 Feb 2024)) further enhances a model’s generalizability and enables interactive open-format behavior.

3. Benchmarking, Evaluation, and Pipeline Best Practices

Open-format tasks necessitate rigorous, multifaceted evaluation protocols:

• Benchmark Design: SLUE (Shon et al., 2021), MMSU (Wang et al., 5 Jun 2025), Dynamic-SUPERB (Huang et al., 8 Nov 2024), and URO-Bench (Yan et al., 25 Feb 2025) provide comprehensive task suites spanning NER, sentiment, translation, semantic reasoning, emotion recognition, and more, often curated for realistic, naturally-produced speech from diverse corpora.
• Evaluation Methods: Metrics include F1, WER, ROUGE, BLEU, BERTScore, MOSNet (subjective quality), MaxMatch M2 and ERRANT F₀.₅ for error correction feedback, and accuracy for classification. Open-format sequence generation (as in SQA, AST (Elmakies et al., 21 Sep 2025)) leverages continuous rewards (e.g., BLEU) in reinforcement learning updates.
• LLM-Based Output Scoring: Dynamic-SUPERB Phase-2 (Huang et al., 8 Nov 2024) employs external LLMs (GPT-4o) as evaluative referees, converting free-form natural language outputs into binary or regression judgments and scaling performance to account for output parsability (handling “N/A” rates in regression).
• Reproducibility and Extensibility: Toolkits such as SLUE-PERB (Arora et al., 14 Jun 2024), OpenSLU (Qin et al., 2023), ESPnet-SE++ (Lu et al., 2022), and WEST (Zhang et al., 24 Sep 2025) offer open-source pipelines, configuration-driven recipes, and pre-trained model checkpoints to facilitate rapid experimentation and benchmarking.

Careful separation of model head tuning (lightweight linear vs. complex encoder-decoder (Arora et al., 14 Jun 2024)), explicit documentation of parameter scalability and latency impacts, and use of standard data formats (JSONL, sequence packing) support reliable comparison and reproducibility.

4. Task Generalization, Instruction Following, and In-Context Adaptability

A defining trait of open-format speech understanding is robustness to task shift, prompt variability, and novel instruction:

• Instruction Tuning and Task-Agnosticity: Instruction tuning with natural language prompts enables univerSLU-type models (Arora et al., 2023) to generalize to paraphrased and dynamically described task scenarios. In some cases, models can handle unseen datasets or languages within the set of task types observed during training, though transfer to unseen task types remains limited.
• Randomized Label Fine-Tuning: To address the challenge of open-format, task-unseen generalization, randomized label fine-tuning (Agrawal et al., 12 May 2025) permutes label-name associations across training mini-batches, compelling the model to attend to instructions and task definitions, rather than memorized label semantics. This yields significant gains in zero-shot and few-shot performance on true “unseen-task” evaluations.
• In-Context Learning and Dialogue State: Benchmarks such as URO-Bench (Yan et al., 25 Feb 2025) and tool-based agents like AURA (Maben et al., 29 Jun 2025) emphasize the need for persistent dialogue state, multi-turn context tracking, and dynamic interleaving of reasoning with action invocation, all evaluated alongside instruction adherence, logical reasoning, and paralinguistic processing.

Empirical evidence shows that instruction tuning and label-randomization methods outperform traditional fine-tuning on mis-matched or unseen tasks, and that open-format specifications in prompts or utterances offer a scalable path for model deployment in real conversational settings.

5. Integration of Paralinguistics, Reasoning, and Tool Use

Progress in open-format understanding is characterized by the increased integration of non-linguistic features (prosody, emotion, speaker identity) and real-world tool usage:

• Paralinguistic and Prosodic Cues: MMSU (Wang et al., 5 Jun 2025) and Dynamic-SUPERB (Huang et al., 8 Nov 2024) include extensive tasks on emotion, speaking style, prosody, and phonological traits. Benchmarking exposes that even state-of-the-art SpeechLLMs (e.g., Gemini-1.5-Pro, Qwen2-Audio) achieve substantially lower than human-level accuracy, especially in low-level perception, highlighting a major research gap.
• Reasoning and Multimodality: Tasks such as open-format spoken question answering, complex dialogue, and instruction following (as seen in Step-Audio (Huang et al., 17 Feb 2025) and AURA (Maben et al., 29 Jun 2025)) require models to integrate higher-level reasoning, context maintenance, dynamic tool invocation, and potentially role-playing abilities. AURA’s system design separates speech input, reasoning (ReAct paradigm), and action execution—supporting tool use for calendar, web, and email through modular action classes and natural language prompt orchestration.
• Agentic Behavior: The emergence of agent-driven pipelines enables complex, goal-oriented tasks in voice-driven, multi-turn settings, evaluated with both formal metrics (e.g., task success rates, VoiceBench OpenBookQA accuracy) and human ratings.

Model designs that map acoustic and linguistic cues directly into LLM reasoning modules, supported by modular plug-and-play tool interfaces, are critical for bridging the gap to robust, general-purpose spoken AI.

6. Limitations, Performance Bottlenecks, and Future Directions

Despite recent progress, evaluation on MMSU (Wang et al., 5 Jun 2025), Dynamic-SUPERB (Huang et al., 8 Nov 2024), URO-Bench (Yan et al., 25 Feb 2025), and SLUE-PERB (Arora et al., 14 Jun 2024) reveals substantial gaps between current system performance and human-level generalization:

• Performance Gaps: Top performing models lag human baselines by significant margins (often >25% difference) on fine-grained perception, paralinguistics, and multi-turn reasoning (Wang et al., 5 Jun 2025). No model in Dynamic-SUPERB excels universally, with each exhibiting weaknesses in specific task clusters (e.g., instruction following, emotion recognition, tool integration).
• Resource and Scalability Challenges: Many breakthroughs remain industry-led, requiring large-scale data and compute. OSUM (Geng et al., 23 Jan 2025) targets this gap by demonstrating how academic-scale models can leverage open training practices, multi-task strategies, and transparent pipelines to approach industrial benchmarks.
• Instructional and In-Context Challenges: Catastrophic forgetting (failing to retain instruction precision across tasks (Yan et al., 25 Feb 2025)), ineffective alignment on long conversational context, and limited transfer to unseen task specifications present ongoing research bottlenecks.
• Improvement Opportunities: Future directions include: enhanced acoustic feature extraction for paralinguistics; systematic multi-task and reinforcement learning with task-specific or continuous reward signals (GRPO (Elmakies et al., 21 Sep 2025)); integration of multimodal streams for richer real-world context; and expansion of evaluation resources (as in ongoing growth of Dynamic-SUPERB’s task suite and human-in-the-loop feedback systems).

Open-source release of model code, data, and evaluation tools—central to WEST (Zhang et al., 24 Sep 2025), Step-Audio (Huang et al., 17 Feb 2025), and AURA (Maben et al., 29 Jun 2025)—is an accelerating trend, supporting reproducibility and collaborative advancement.

7. Summary Table of Representative Benchmarks and Models

Model/Benchmark	Architectural Type / Scope	Key Open-Format Tasks
SpeechNet (Chen et al., 2021)	Modular, recurrent/conformer-based MTL	ASR, SE, SC, TTS, VC
SLUE (Shon et al., 2021), SLUE-PERB (Arora et al., 14 Jun 2024)	Benchmark suite / evaluation toolkit	NER, sentiment, ASR, QA
UniverSLU (Arora et al., 2023)	Whisper-based, instruction-tuned encoder-decoder	12+ SLU tasks, zero-shot
WEST (Zhang et al., 24 Sep 2025)	LLM + robust speech encoder (TouchASU)	Recognition, Q&A, attribute extraction
MMSU (Wang et al., 5 Jun 2025)	Benchmark—47 tasks (perceptual & reasoning)	Phonetics, semantics, paralinguistics
AURA (Maben et al., 29 Jun 2025)	Cascaded ASR–LLM–TTS agent, modular tools	Speech QA, tool use, dialogue
URO-Bench (Yan et al., 25 Feb 2025)	S2S benchmark—multi-turn, multilingual, paralinguistics	Understanding, reasoning, dialogue
Step-Audio (Huang et al., 17 Feb 2025)	Unified, 130B multi-modal speech-text LLM	Recognition, synthesis, chat
OSUM (Geng et al., 23 Jan 2025)	Whisper encoder + Qwen2 LLM, multi-task	ASR+X (emotion, style, age/gender, dialog)

Conclusion

The rapid evolution of open-format speech understanding is driven by advances in unified, modular model architectures, comprehensive and granular benchmarks, task-agnostic training principles, and transparent toolkits. Models are increasingly evaluated across a spectrum of linguistic, paralinguistic, and reasoning-based tasks using high-fidelity human and automatic metrics. Despite substantial progress, persistent limitations in multi-turn context handling, paralinguistics, and zero-shot task generalization remain. The continued interplay of large-scale supervised and self-supervised pre-training, multi-task and reinforcement learning, and cross-disciplinary benchmarking is expected to drive future improvements in practical, human-level open-format speech understanding systems.