voice-optimization

Voice Scenario Optimization

R — Source Text (Reading)

Perplexity Voice requires "speak quickly," supports only English, prohibits speaker identification, prohibits singing and humming, prohibits impersonation; Claude Mobile emphasizes "always lead with the answer, no preamble," lists are easier to scan on small screens; Sesame AI Maya is a conversation mode optimized specifically for voice. Core patterns: brevity first, colloquial adaptation, audio safety constraints, language restrictions, removal of visual formatting.

I — Methodology Skeleton (Interpretation)

1. Brevity-first principle: In voice scenarios, the user's attention window is extremely short, so answers must get straight to the point, and preamble-style expressions such as "OK, let me answer your question" are prohibited.
2. Colloquial conversion: Convert written language into natural spoken language—use short sentences, active voice, and everyday vocabulary, and avoid nested clauses and piling up terminology.
3. Format downgrade: Remove visual formats such as Markdown tables, code blocks, and nested lists, and replace them with natural language descriptions or short enumerations.
4. Audio safety constraints: Prohibit identifying the identity of a specific speaker, prohibit imitating the vocal characteristics of real people, and prohibit singing or humming melodies.
5. Language restriction statement: Clearly state the supported language range, and when outside that range, guide users to adjust settings rather than forcing processing.
6. Length adaptation: Dynamically adjust answer length according to question complexity—one or two sentences for simple questions, and complex questions kept within a reasonable duration.

A1 — Case Analysis (Past Application)

Case: Perplexity Voice's audio safety boundaries

• Problem: In voice interaction, users may ask to imitate a celebrity's voice, identify the person on a call, or ask the AI to sing, and these behaviors involve privacy, copyright, and safety risks.
• Use of the design pattern: Perplexity Voice sets clear prohibitions in the system prompt—no speaker identification from voice input ("No speaker identification from voice"), no execution of singing or humming requests, and no impersonation of people. At the same time, it limits support to English only, and when beyond its capabilities, guides users to modify settings.
• Conclusion: Voice scenarios have unique safety boundaries (voiceprints, impersonation, singing) that do not exist in pure text scenarios and require dedicated protection.

Case: Claude Mobile's answer-first strategy

• Problem: In mobile voice answers, users quickly lose patience when hearing lengthy opening remarks, especially in scenarios such as driving or walking.
• Use of the design pattern: Claude Mobile explicitly instructs "Always lead with answer. No preamble.", putting the answer first and the explanation afterward. It sets length tiers for questions of different complexity—1-2 sentences for simple questions, short lists for operational guides, and 2-3 paragraphs for substantive questions.
• Conclusion: Voice scenarios are extremely sensitive to perceived latency, and removing preamble can significantly improve user satisfaction and information acquisition efficiency.

A2 — Trigger Scenarios (Future Trigger) ★

In what situations do users need this?

1. Design system prompts for voice assistants (such as smart speakers and in-car assistants)
2. Add a voice interaction mode to an existing text chatbot
3. Build a dialogue script for a telephone customer service AI
4. Optimize spoken expression in podcast generation or audio content synthesis

Language Signals

• "Output optimization in voice scenarios"
• "Needs conversational answers"
• "The user asks questions by voice"
• "The answer will be read aloud"
• "How to make AI speech more natural"

Distinction from adjacent skills

• Difference from mobile-adaptation : mobile adaptation focuses on screen size constraints, while voice optimization focuses on auditory channel constraints; however, both emphasize conciseness first and are often used together
• Difference from citation-system : the citation system requires special handling in voice scenarios (visual markers cannot be used), but voice optimization does not involve citation format design itself

E — Executable Steps (Execution)

1. Step 1: Set answer length tiers - Completion criteria: define 3-4 tiers for question complexity (simple/operational/moderate/complex), specify the maximum number of sentences or estimated reading time for each tier, and explain them with examples in the system prompt.
2. Step 2: Write preamble prohibitions and answer-first rules - Completion criteria: explicitly state "do not add confirmatory preambles at the beginning of the answer," and provide a comparison of correct and incorrect examples (e.g., incorrect: "Okay, let me answer that for you..."; correct: give the answer directly).
3. Step 3: Define format degradation rules - Completion criteria: list the visual formats that need to be degraded (tables → natural language descriptions, nested lists → flat enumeration, code blocks → conversational step-by-step explanations), and give an example of each degradation.
4. Step 4: Set audio safety boundaries - Completion criteria: clearly define the list of prohibited behaviors (speaker identification, voice imitation, singing or humming, character role-play), and define standard fallback wording when beyond capability.
5. Step 5: Add conversational conversion guidelines - Completion criteria: list the conversion rules from written language to spoken language (subordinate clauses → short sentences, passive → active, terminology → everyday vocabulary), and provide more than 3 conversion examples.

B — Boundary (Boundary) ★

Do not use in the following situations

• Pure text chat interfaces, where users input via keyboard and read on screen
• Technical selection or parameter tuning of speech synthesis (TTS) engines
• Audio signal processing (noise reduction, echo cancellation, etc.)
• Multimodal scenarios where voice is only an auxiliary channel (such as subtitle scenarios in video conferencing)

Common failure modes

• Copying text output: directly using text chat replies in voice scenarios, resulting in lengthy preambles and visual formats (tables, code blocks) being read aloud, leading to an extremely poor user experience
• Ignoring audio-specific risks: only optimizing the expression style but not setting audio-specific safety boundaries such as speaker identification and imitation
• Over-simplification: compressing all answers into one sentence, losing necessary information and context; answers should be tiered by complexity rather than handled with a one-size-fits-all approach
• Ignoring language limitations: not stating the supported language range, leading to chaotic output or degraded quality in multilingual scenarios