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Listening comprehension is the computational interpretation of spoken audio to recover information conveyed through speech and other acoustically expressed cues. It comprises the transformation of a time-varying speech signal into representations from which a system can identify linguistic content—including phonetic, lexical, syntactic, and semantic structure—and, when required by the task, infer speaker intent, dialogue acts, affective or paralinguistic attributes, and context-dependent meaning ^[1][2]. The process may be implemented as a pipeline that combines speech recognition with subsequent language understanding, or as an end-to-end model that maps acoustic representations directly to semantic outputs ^[2][3]. Unlike acoustic event detection, which identifies the presence or class of sounds, listening comprehension concerns the interpretation of information encoded in spoken utterances. It also differs from automatic speech recognition: speech recognition primarily produces a textual or symbolic rendering of speech, whereas listening comprehension requires the extraction or inference of task-relevant meaning from the signal, with transcription serving only as a possible intermediate representation ^[1][3].