speechrather

Speechrather is a theoretical framework in the fields of computational linguistics and human–computer interaction that seeks to identify and model speaker preferences as they are expressed in spoken language. The name signals its emphasis on preference construction that can appear through choices, contrasts, and evaluative language, including constructions using rather or similar contrasts. In speechrather, utterances are analyzed through three layers: acoustic/prosodic features (intonation, stress, rhythm), lexical-syntactic cues (word choice, sentence structure), and discourse-level context (topic, prior turns). The framework aims to produce a ranked representation of candidate options reflecting the speaker’s stated or implied preferences.

Architecture and methods: An acoustic processing module extracts prosodic cues; a discourse interpretation module tracks options discussed in dialogue; a preference inference module aggregates evidence from prosody and text to output a preference ordering. Probabilistic or neural models may be used to combine signals, while constraint-based rules capture conventional language patterns for expressing preference (for example, “I would rather have X than Y”).

Applications: Speechrather could inform adaptive dialogue systems that tailor responses to user preferences, assistive technologies for individuals with communication needs, and market research tools that infer consumer preferences from spoken interactions.

Limitations: Performance depends on audio quality, labeling of preferences can be ambiguous, and there are privacy considerations in recording and analyzing spoken preference data.

See also: pragmatics, prosody, preference elicitation, dialogue systems.