Who Spoke What When? Evaluating Spoken Language Models for Conversational ASR with Semantic and Overlap-Aware Metrics

• URL: http://arxiv.org/abs/2603.22709v1
• Date: Tue, 24 Mar 2026 02:01:21 GMT
• Title: Who Spoke What When? Evaluating Spoken Language Models for Conversational ASR with Semantic and Overlap-Aware Metrics
• Abstract summary: Conversational automatic speech recognition remains challenging due to overlapping speech, far-field noise, and varying speaker counts.<n>Recent LLM-based systems perform well on single-speaker benchmarks, but their robustness in multi-speaker settings is unclear.<n>We systematically compare LLM-based and modular approaches along four axes: overlap, semantic fidelity, speaker count, and single- versus multi-channel input.
• Score: 99.89493037369071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational automatic speech recognition remains challenging due to overlapping speech, far-field noise, and varying speaker counts. While recent LLM-based systems perform well on single-speaker benchmarks, their robustness in multi-speaker settings is unclear. We systematically compare LLM-based and modular pipeline approaches along four axes: overlap robustness, semantic fidelity, speaker count, and single- versus multi-channel input. To capture meaning-altering errors that conventional metrics miss, we introduce tcpSemER, which extends tcpWER by replacing Levenshtein distance with embedding-based semantic similarity. We further decompose tcpWER into overlapping and non-overlapping components for finer-grained analysis. Experiments across three datasets show that LLM-based systems are competitive in two-speaker settings but degrade as speaker count and overlap increase, whereas modular pipelines remain more robust.

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