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This paper proposes the Conversational Distribution Score (CDS), a new metric framework for evaluating spoken conversational systems by comparing behavioral distributions against human references. By analyzing features like speech rate and turn interaction, it offers an interpretable method to assess synthetic dialogue quality that complements existing specific interactional metrics. This advancement addresses a critical gap in objectively measuring how closely AI-driven voice agents mimic natural human conversation patterns.
Distributional Metrics for Evaluating Spoken Conversational Systems
arXiv:2609.31719v1 Announce Type: cross Abstract: Evaluating conversational systems is a difficult and unresolved problem. We introduce the Conversational Distribution Score (CDS), which compares distributions of conversational behaviour using human conversations as a reference. CDS describes speech rate, syllabic rhythm, and turn interaction through eight interpretable features plus a separate two-feature semantic baseline. We compare conversations with two reference scales: one based on conversational success within human dialogue and another contrasting human and synthetic dialogue. Using listener judgments from out-of-domain goal--oriented dialogues, we examine system ranking, preferences between conversations, and ranking stability. Composite CDS recovers five of six listener system comparisons while individual features show strong correlation with listener preferences between conversations. We examine how many minutes and conversations are required before rankings stabilize. These findings support distributional comparisons as a complement to specific interactional metrics to evaluate conversations and conversational models while showing their interpretable value.
Abstract:Evaluating conversational systems is a difficult and unresolved problem. We introduce the Conversational Distribution Score (CDS), which compares distributions of conversational behaviour using human conversations as a reference. CDS describes speech rate, syllabic rhythm, and turn interaction through eight interpretable features plus a separate two-feature semantic baseline. We compare conversations with two reference scales: one based on conversational success within human dialogue and another contrasting human and synthetic dialogue. Using listener judgments from out-of-domain goal--oriented dialogues, we examine system ranking, preferences between conversations, and ranking stability. Composite CDS recovers five of six listener system comparisons while individual features show strong correlation with listener preferences between conversations. We examine how many minutes and conversations are required before rankings stabilize. These findings support distributional comparisons as a complement to specific interactional metrics to evaluate conversations and conversational models while showing their interpretable value.
5 pages, 3 figures, 1 table. Submitted to IEEE ICASSP 2027
Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
arXiv:2609.31719 [eess.AS]
(or arXiv:2609.31719v1 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2609.31719 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 252 words
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