https://livekit.io logo
Join Slack
Powered by
how make tts response relastic
# ask-ai
p
how make tts response relastic
✅ 1
t
Thanks for reaching out, arpan. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I’ll help you make TTS responses sound more realistic. A few quick, effective steps you can apply now: • Use a system prompt that teaches the model to speak conversationally (include examples of natural phrasing, filler words, and mid-sentence corrections). • Add concrete examples in the prompt showing the exact disfluencies you want (e.g., “Yeah, um — I can do that,” not “I can do that”). • Use SSML to control timing and prosody: ◦ Insert pauses with `break time="200ms"/`–`300ms` after fillers like “um” or “uh.” ◦ Use 
<prosody rate="...">
and 
<pitch>
for small, natural variations. • Define personality as audible behaviors (rules like “start some answers with ‘So,’ use ‘yeah’ instead of ‘yes’ occasionally”) rather than vague adjectives. • Constrain emotion: set a baseline (e.g., calm) and allow stronger emotions only in specific situations. • Add 
[laughter]
or short emotional tags where appropriate for authenticity. • If you need richer prosody and hesitation, prefer a real-time speech model (S2S). If using a cascaded pipeline (STT → LLM → TTS), rely heavily on prompt + SSML to preserve naturalness. • Iterate: record samples, adjust prompt and SSML (pause lengths, filler placement), and A/B test voices and prosody settings. If you’d like, tell me: • Which TTS engine you’re using and whether you’re on real-time S2S or a cascaded pipeline. • One short example phrase you want to make sound more natural. I’ll give a tailored prompt + SSML example and recommended settings. Sources: Prompting voice agents to sound more realistic | LiveKit | Pipeline vs. Realtime - Which is the better Voice Agent Architecture? | Liv... | Pipeline vs. Realtime - Which is the better Voice Agent Architecture? | Liv..., with interactive elements
p
one prompt example
t
Thanks — here’s a single, ready-to-use system prompt you can feed into your LLM (or into the text stage of an STT→LLM→TTS pipeline). It includes conversational rules, filler placement, SSML examples, and emotion constraints. System prompt: You are a conversational assistant speaking aloud. Speak naturally, like a calm, friendly person answering someone in real time. Follow these rules: • Use informal spoken phrasing (start some sentences with “So,” use “yeah/nah/okay”) and occasionally break grammar for realism. • Insert short filler words sparingly (e.g., “um,” “uh,” “yeah”) to sound human—no more than one filler per sentence. • After each standalone filler, add a short pause using SSML: 
<break time="250ms"/>
. • Use brief, varied pauses to simulate thinking: prefer 150–350ms for mid-sentence pauses and 400–700ms for sentence boundaries. • Vary pitch and speed slightly for emphasis using SSML 
<prosody>
tags; keep changes subtle (±5–10%). • Keep overall emotion calm and friendly. Only introduce stronger emotions when explicitly requested. • Add short non-verbal cues where appropriate: 
[laughter]
, 
[sigh]
. • Keep responses concise (1–3 short sentences) unless the user asks for detail. Examples (produce exactly like these, with SSML included): 1) Short answer with filler: So, yeah, break time="250ms"/ I can set that up for you. 2) Thoughtful pause: Uh break time="250ms"/ let me check that—break time="300ms"/ I think we can do it. 3) Emphasized point: Okay, prosody rate="95%" pitch="+3%"here’s what I suggest:/prosody break time="200ms"/ First, try X. When generating final output, wrap spoken text in SSML so the TTS engine preserves pauses and prosody. Only include stronger emotion tags or longer laughter when context clearly supports them. , with interactive elements
4 Views
Open in Slack
PreviousNext
Powered by