Resolving egocentric spatial expressions in conversational navigation requires mapping speaker-centric references to allocentric orientations. In practice, this process is often mediated by automatic speech recognition (ASR), where transcription errors, linguistic variation, and referential ambiguity obscure spatial relations. Despite progress in navigation and multimodal reasoning, egocentric spatial reference resolution under ASR-transcribed, non-English conversational input remains underexplored. We introduce Conversational Orientation Reasoning (COR), a diagnostic benchmark for egocentric spatial reference resolution in Traditional Chinese conversational navigation. COR consists of 10,000 instances pairing ASR-transcribed utterances with structured landmark coordinates derived from real-world environments projected onto a 50 x 50 grid. COR isolates the effects of spoken language noise from geometric computation, enabling evaluation of transcription errors, linguistic variation, and referential ambiguity beyond end-to-end navigation success. Using COR, we study an interpretable decomposition of orientation inference into three steps: extracting spatial relations, mapping landmark coordinates to absolute directions, and inferring the speaker’s orientation. Models following this decomposition achieve 99.6% accuracy on clean text and retain 95.7% accuracy under ASR-transcribed input. COR supports analysis of failure modes in spoken spatial reference resolution, providing insights into how language grounding degrades under noisy conversational input. The code and dataset are publicly available under the MIT license.