KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMs
arXiv:2605.27984v1 Announce Type: cross Abstract: Speech language models (SpeechLMs) have achieved substantial progress by extending large language models (LLMs) to the speech modality. However, SpeechLM evaluation remains heavily centered on English, limiting reliable assessment of multilingual speech capabilities. Straightforward benchmark transfer through ASR, translation, normalization, and TTS can corrupt language-specific instructions, answer constraints, and spoken forms; for audio understanding, transferring source-language audio also fails to preserve target-language speaker attribute
The proliferation of Large Language Models (LLMs) and their extension to speech modalities (SpeechLMs) necessitates benchmarks that move beyond English-centric evaluations, especially as AI adoption globalizes.
This development highlights the critical need for robust, language-specific benchmarks to accurately assess multilingual speech AI capabilities, preventing biases and enabling wider, more equitable AI integration.
The introduction of agent-driven Korean speech benchmarks shifts evaluation paradigms towards preserving language-specific nuances, moving beyond crude translation-based transfer methods for SpeechLMs.
- · Korean AI developers
- · Multilingual AI research
- · SpeechLM developers
- · English-only SpeechLM evaluation approaches
- · Companies with limited multilingual AI data sets
Improved performance and reliability of SpeechLMs for non-English languages, starting with Korean.
Increased investment and research into agent-driven, language-specific AI benchmarks across various modalities and languages.
Accelerated development of truly multilingual AI systems that can natively understand and generate speech in diverse linguistic contexts, reducing digital language barriers.
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Read at arXiv cs.AI