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Speech-1, TTS to reduce AI call drop-rate

Hacker News

Speech-1, TTS to reduce AI call drop-rate

We’re releasing access to Speech-1, our conversational speech model designed specifically for customer phone calls (8khz telephony) to reduce call drop rates. Try it: https://tts.metavoice.io Samples: https://www.metavoice.io Voice AI calls today face >30% call drop rates with current synthetic voices. We want to make this tend to 0 by making AI speech far more human-like and engaging. Current synthetic voices are overly perfect, and don’t sound like the typical human phone agent. They have audiobook-like narration, erratic emotions (e.g. over-enthusiastic “Got it!”), and lack natural-sounding imperfections (like breaths, hmm, umm). They also can’t reliably speak numbers, spellings, emails, etc. (& often produce chipmunk like sped-up speech). Speech-1 fixes these problems! We’re also releasing a research preview of Speech-1.1. It’s trained using ProsodyAlign, a novel post-training technique to more closely capture human-like speech patterns – pauses, stress, and how the voice goes up/down in tone on different words. However, it is currently less stable than Speech-1. For more details, read our blogpost: https://www.metavoice.io/blog-posts/conversational-speech-mo...

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, email · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, training · Missing: mrr, revenue, profit
32%32% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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