aiqbench analysis
This paper addresses a critical gap in speech large language models by introducing LibriSpeech-Real-QA, a benchmark designed to evaluate streaming question-answering capabilities rather than offline processing. The authors demonstrate that current commercial models often fail to respond until the entire audio segment is received, while their proposed modeling designs establish a viable baseline for real-time interaction. This work provides essential evaluation protocols for advancing SLMs toward practical, low-latency applications in continuous speech environments.
LibriSpeech-Real-QA: A Benchmark for Streaming Question Answering
arXiv:2611.06496v1 Announce Type: new Abstract: Most speech large language models (SLMs) still process speech in an offline manner. They only start responding after they receive the complete speech segment. However, many real-world applications require streaming speech interaction, for which the audio continuously flows in and the machine is required to answer questions based on the so far received audio chunks. For example, a user may ask the system to continuously count how many times a person's name has been mentioned while they are speaking. We observe a lack of benchmarks and evaluation protocols for real-time speech interaction. Motivated by this, we propose LibriSpeech-Real-QA, a dataset and a benchmark for real-time speech interaction. We construct different types of real-time question-answer pairs from LibriSpeech to evaluate whether speech LLMs can identify target words in streaming audio. We evaluate several popular real-time commercial models and find that they still tend to respond only after the speech turn has finished. We demonstrate that we can enable a pretrained SLM to perform this task, where we explore several modeling designs and establish a solid baseline for future research.
Abstract:Most speech large language models (SLMs) still process speech in an offline manner. They only start responding after they receive the complete speech segment. However, many real-world applications require streaming speech interaction, for which the audio continuously flows in and the machine is required to answer questions based on the so far received audio chunks. For example, a user may ask the system to continuously count how many times a person's name has been mentioned while they are speaking. We observe a lack of benchmarks and evaluation protocols for real-time speech interaction. Motivated by this, we propose LibriSpeech-Real-QA, a dataset and a benchmark for real-time speech interaction. We construct different types of real-time question-answer pairs from LibriSpeech to evaluate whether speech LLMs can identify target words in streaming audio. We evaluate several popular real-time commercial models and find that they still tend to respond only after the speech turn has finished. We demonstrate that we can enable a pretrained SLM to perform this task, where we explore several modeling designs and establish a solid baseline for future research.
5 pages, 1 figure, 6 tables. Submitted to IEEE ICASSP 2027
Computation and Language (cs.CL)
arXiv:2611.06496 [cs.CL]
(or arXiv:2611.06496v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.06496 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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