LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech
Abstract
Recent advances in audio-language models have demonstrated remarkable success on short, segment-level speech tasks. However, real-world applications such as meeting transcription, spoken document understanding, and conversational analysis require robust models capable of processing and reasoning over long-form audio. In this work, we present LongSpeech, a large-scale and scalable benchmark specifically designed to evaluate and advance the capabilities of speech models on long-duration audio. LongSpeech comprises over 100,000 speech segments, each approximately 10 minutes long, with rich annotations for ASR, speech translation, summarization, language detection, speaker counting, content separation, and question answering. We introduce a reproducible pipeline for constructing long-form speech benchmarks from diverse sources, enabling future extensions. Our initial experiments with state-of-the-art models reveal significant performance gaps, with models often specializing in one task at the expense of others and struggling with higher-level reasoning. These findings underscore the challenging nature of our benchmark. Our benchmark will be made publicly available to the research community.
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 work is the introduction of LongSpeech, a large-scale benchmark designed to evaluate long-form speech processing capabilities, which addresses significant gaps in current audio-language models. The paper's comprehensive methodology and experimental evaluation underscore its potential to catalyze advancements in the field, although improvements in reproducibility and accessibility would enhance its impact further.
Methodology
The methodology presented in the paper is robust, with a clear focus on constructing a large-scale benchmark for long-form speech processing. The authors detail their approach to dataset construction, including the integration of diverse data sources and the careful curation of audio segments. The multi-task nature of the benchmark is a significant strength, addressing various aspects of speech understanding, including ASR, translation, summarization, and more. The reproducibility of the dataset construction process is emphasized, which is critical for future research.
Results & evaluation
The experimental evaluation is thorough, utilizing multiple state-of-the-art models to benchmark performance across the various tasks. The results highlight significant performance gaps in current models, particularly in higher-level reasoning tasks. The use of established evaluation metrics (e.g., WER, BLEU, ROUGE) adds credibility to the findings, although the paper could benefit from a more detailed discussion of the experimental setup and model configurations.
Reproducibility
While the authors mention a reproducible pipeline for constructing the benchmark, the paper lacks specific implementation details or links to code repositories that would facilitate direct replication of the results. This is a notable gap, as reproducibility is crucial for the validation of research findings.
Limitations
The paper acknowledges limitations in the current models' performance, particularly in tasks requiring deep semantic understanding and reasoning over long audio contexts. However, it does not address potential biases in the dataset or the limitations of the models used in the experiments. Additionally, the lack of a publicly accessible demo or project URL diminishes the immediate applicability of the benchmark.
Broader impact
The LongSpeech benchmark has the potential to significantly advance the field of long-form speech processing, addressing a critical gap between existing benchmarks and real-world applications. By providing a comprehensive evaluation framework, it encourages the development of more robust audio-language models capable of handling complex, real-world audio scenarios. This could have implications in various domains, including meeting transcription, spoken document understanding, and conversational AI. The main contribution of this work is the introduction of LongSpeech, a large-scale benchmark designed to evaluate long-form speech processing capabilities, which addresses significant gaps in current audio-language models. The paper's comprehensive methodology and experimental evaluation underscore its potential to catalyze advancements in the field, although improvements in reproducibility and accessibility would enhance its impact further.
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