SQ-LLM, trained on the new SpeechEval dataset of 32k multilingual clips with 128k annotations, enables LLMs to perform interpretable multi-task speech quality evaluation including assessment, comparison, improvement suggestions, and deepfake detection.
Objectively, intelligibility and dynamic range are equivalent, though Sample A has faint artifact distortion (0-5 s), and Sample B contains minimal background noise (0-5 s)
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SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation
SQ-LLM, trained on the new SpeechEval dataset of 32k multilingual clips with 128k annotations, enables LLMs to perform interpretable multi-task speech quality evaluation including assessment, comparison, improvement suggestions, and deepfake detection.