SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation
This addresses the need for more reliable evaluation in speech generation for human-computer interaction, though it is incremental as it builds on existing benchmarks.
The paper tackled the problem of evaluating paralinguistic cues in Large Audio-Language Models by introducing SpeechParaling-Bench, a benchmark that expands feature coverage and uses a pairwise comparison pipeline, revealing that leading models struggle with control and modulation, with 43.3% of errors in situational dialogue due to misinterpretation.
Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and the inherent subjectivity of assessment. To address these challenges, we introduce SpeechParaling-Bench, a comprehensive benchmark for paralinguistic-aware speech generation. It expands existing coverage from fewer than 50 to over 100 fine-grained features, supported by more than 1,000 English-Chinese parallel speech queries, and is organized into three progressively challenging tasks: fine-grained control, intra-utterance variation, and context-aware adaptation. To enable reliable evaluation, we further develop a pairwise comparison pipeline, in which candidate responses are evaluated against a fixed baseline by an LALM-based judge. By framing evaluation as relative preference rather than absolute scoring, this approach mitigates subjectivity and yields more stable and scalable assessments without costly human annotation. Extensive experiments reveal substantial limitations in current LALMs. Even leading proprietary models struggle with comprehensive static control and dynamic modulation of paralinguistic features, while failure to correctly interpret paralinguistic cues accounts for 43.3% of errors in situational dialogue. These findings underscore the need for more robust paralinguistic modeling toward human-aligned voice assistants.