HPSU: A Benchmark for Human-Level Perception in Real-World Spoken Speech Understanding
Abstract
Recent advances in Speech Large Language Models (Speech LLMs) have led to great progress in speech understanding tasks such as Automatic Speech Recognition (ASR) and Speech Emotion Recognition (SER). However, whether these models can achieve human-level auditory perception, particularly in terms of their ability to comprehend latent intentions and implicit emotions in real-world spoken language, remains underexplored. To this end, we introduce the Human-level Perception in Spoken Speech Understanding (HPSU), a new benchmark for fully evaluating the human-level perceptual and understanding capabilities of Speech LLMs. HPSU comprises over 20,000 expert-validated spoken language understanding samples in English and Chinese. It establishes a comprehensive evaluation framework by encompassing a spectrum of tasks, ranging from basic speaker attribute recognition to complex inference of latent intentions and implicit emotions. To address the issues of data scarcity and high cost of manual annotation in real-world scenarios, we developed a semi-automatic annotation process. This process fuses audio, textual, and visual information to enable precise speech understanding and labeling, thus enhancing both annotation efficiency and quality. We systematically evaluate various open-source and proprietary Speech LLMs. The results demonstrate that even top-performing models still fall considerably short of human capabilities in understanding genuine spoken interactions. Consequently, HPSU will be useful for guiding the development of Speech LLMs toward human-level perception and cognition.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the HPSU benchmark, which systematically evaluates the human-level perceptual and understanding capabilities of Speech LLMs in real-world spoken language contexts. This comprehensive analysis of the technical contributions, methodology, and significance to the field underscores the importance of addressing the limitations of current models and guiding future research towards achieving more sophisticated speech understanding.
Methodology
The paper presents a well-structured methodology for constructing the HPSU benchmark, incorporating a semi-automatic annotation process that integrates audio, text, and visual modalities. This innovative approach addresses the challenges of data scarcity and high costs associated with manual annotation, enhancing both efficiency and quality. The hierarchical taxonomy of tasks and the adversarial induction protocol for robustness testing are commendable features that significantly improve the evaluation framework.
Results & evaluation
The experimental evaluation is comprehensive, involving 13 leading models and a detailed analysis of their performance across various tasks. The results highlight the significant gap between human capabilities and those of current Speech LLMs, particularly in complex reasoning tasks. The use of a human baseline and random guessing as benchmarks provides a clear context for interpreting model performance.
Reproducibility
The paper provides sufficient details about the datasets, annotation process, and evaluation metrics, which would allow other researchers to replicate the study. However, the lack of specific details regarding the training and evaluation of the models limits full reproducibility.
Limitations
The paper acknowledges limitations related to the performance of Speech LLMs, particularly their struggles with complex semantic reasoning and susceptibility to misleading prompts. Additionally, the reliance on specific datasets may introduce biases that affect generalizability.
Broader impact
The HPSU benchmark has the potential to significantly influence research in speech understanding by providing a rigorous evaluation framework that encourages the development of models capable of human-level perception. This could lead to advancements in applications such as human-computer interaction, sentiment analysis, and multilingual communication. The main contribution of this paper is the introduction of the HPSU benchmark, which systematically evaluates the human-level perceptual and understanding capabilities of Speech LLMs in real-world spoken language contexts. This comprehensive analysis of the technical contributions, methodology, and significance to the field underscores the importance of addressing the limitations of current models and guiding future research towards achieving more sophisticated speech understanding.
Author of this paper? Send a correction.