Towards Fine-Grained Multi-Dimensional Speech Understanding: Data Pipeline, Benchmark, and Model
Abstract
While speech Large Language Models (LLMs) excel at conventional tasks like basic speech recognition, they lack fine-grained, multi-dimensional perception. This deficiency is evident in their struggle to disentangle complex features like micro-acoustic cues, acoustic scenes, and paralinguistic signals. This resulting incomplete comprehension of real-world speech fundamentally bottlenecks the development of perceptive and empathetic next-generation speech systems. At its core, this persistent perceptual limitation primarily stems from three interacting factors: scarce high-quality expressive data, absent fine-grained modeling for multi-dimensional attributes, and reliance on restricted coverage, coarse-grained benchmarks. We address these challenges through three pillars: First, our robust data curation pipeline resolves complex acoustic environments and long-audio timestamp alignment challenges to extract a high-quality spontaneous speech corpus from audiovisual sources. Second, we construct FMSU-Bench, a pioneering benchmark covering 14 speech attribute dimensions to rigorously assess the fine-grained, multi-dimensional speech understanding capabilities of current models. Third, empowered by our curated corpus, we introduce FM-Speech. Driven by a decoupled attribute modeling and progressive curriculum fine-tuning framework, it substantially elevates fine-grained, multi-dimensional acoustic perception. Extensive evaluations on FMSU-Bench reveal that current speech LLMs still require significant improvement in multi-dimensional, fine-grained understanding. In contrast, FM-Speech substantially outperforms current open-source models, establishing a robust paradigm for real-world speech understanding.
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 paper makes a significant contribution to the field of machine learning by addressing the limitations of existing speech models and proposing a comprehensive framework for fine-grained multi-dimensional speech understanding. The innovative methodologies and rigorous evaluations presented establish a strong foundation for future research in this area.
Methodology
The paper introduces a comprehensive methodology for fine-grained multi-dimensional speech understanding through a robust data curation pipeline, a pioneering benchmark (FMSU-Bench), and a novel model (FM-Speech). The data pipeline effectively addresses the challenges of extracting high-quality spontaneous speech from audiovisual sources, while the benchmark provides a structured framework for evaluating speech models across 14 distinct dimensions. The progressive curriculum fine-tuning framework for FM-Speech is particularly innovative, allowing for the decoupling of complex auditory attributes and improving model performance on fine-grained tasks.
Results & evaluation
The experiments conducted on FMSU-Bench demonstrate the effectiveness of the proposed methods. The evaluation includes a systematic comparison of FM-Speech against 11 advanced speech LLMs, showcasing significant improvements in multi-dimensional understanding. The rigorous data filtering and human verification processes enhance the reliability of the benchmark, while the use of innovative evaluation metrics (such as PATA) adds depth to the assessment of model performance.
Reproducibility
The paper provides detailed descriptions of the methodology, data curation pipeline, and experimental setup, which contribute to reproducibility. However, the lack of access to the proprietary models used for comparison may limit the ability of others to fully replicate the results. The project URL provides access to the code and resources, which is beneficial for reproducibility.
Limitations
One limitation is the reliance on specific audiovisual sources (movies and TV shows), which may not fully represent the diversity of real-world speech. Additionally, while the benchmark covers a wide range of speech attributes, the complexity of human speech may still present challenges in capturing all nuances. The paper also does not address potential biases in the data or the models used.
Broader impact
The advancements presented in this paper have significant implications for the development of next-generation speech systems that require fine-grained understanding and empathy in human-computer interactions. The establishment of FMSU-Bench sets a new standard for evaluating speech models, potentially influencing future research and applications in audio processing, speech recognition, and human-computer interaction. The paper makes a significant contribution to the field of machine learning by addressing the limitations of existing speech models and proposing a comprehensive framework for fine-grained multi-dimensional speech understanding. The innovative methodologies and rigorous evaluations presented establish a strong foundation for future research in this area.
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