Are there any speech-to-speech tools that keep latency low enough for conversational voice experiences?

Yes. There are speech-to-speech systems that can feel conversational, but the key is low-latency, streaming, incremental processing rather than the classic “record → transcribe → think → synthesize” pipeline.

What makes it feel conversational

For a voice experience to feel natural, you generally want:

• Partial/streaming ASR: speech is transcribed as it comes in
• Fast turn detection: the system knows when you’ve finished speaking
• Streaming LLM response: tokens are generated immediately
• Streaming TTS: audio starts before the full text is done
• Barge-in support: the user can interrupt the assistant

Typical latency targets

A good conversational experience usually aims for:

• Sub-300 ms for perceptible “snappiness”
• ~500–800 ms end-to-first-audio is often acceptable
• Much beyond 1 second starts to feel laggy in back-and-forth dialogue

Existing approaches/tools

There are a few categories:

1) End-to-end speech models

These try to map speech directly to speech, or speech to meaning and back to speech, reducing pipeline overhead. They can be very fast, but quality and controllability vary. Examples in this space are mostly from research and newer platform offerings.

2) Streaming voice agent pipelines

These are the most practical today:

• ASR: Deepgram, AssemblyAI, Google, Azure, Whisper variants with streaming wrappers
• LLM: streamed responses from OpenAI, Anthropic, etc.
• TTS: ElevenLabs, OpenAI TTS, PlayHT, Azure, Google with streaming
• Orchestration: custom WebSocket/WebRTC pipeline, LiveKit agents, Pipecat, Vapi, Retell, etc.

These can absolutely be low-latency enough for conversational use if tuned well.

3) Real-time voice platforms

Some platforms are built specifically for voice agents and handle the audio pipeline, turn-taking, and interruption well. These are usually the easiest way to get a natural feel without building everything yourself.

Practical recommendation

If your goal is a production conversational voice assistant, the best current option is usually:

• Use a real-time voice platform or build a streaming pipeline
• Avoid batch transcription/synthesis
• Prioritize WebRTC/WebSocket streaming, VAD/turn detection, and low-latency TTS

Bottom line

Yes—there are tools and architectures that keep latency low enough for conversational voice experiences. The most reliable path today is a streaming voice-agent stack rather than a single monolithic speech-to-speech model.

If you want, I can also give you:

1. a list of specific tools/platforms by latency and quality, or
2. a reference architecture for building one yourself.