Assistant Voice Features

Benched.ai Editorial Team

Voice features enable an AI assistant to produce or understand spoken language. They span text-to-speech (TTS), automatic speech recognition (ASR), emotion control, and prosody adjustments.

Feature Catalog

Capability	Input / Output	Typical Controls	Example API
Automatic Speech Recognition	Audio → text	Language hint, punctuation toggle	Whisper v3, Google Speech-to-Text
Text-to-Speech	Text → audio	Voice ID, speed, pitch	Amazon Polly Neural, ElevenLabs
Emotion tagging	Text + emotion → audio	Emotion label (happy, sad)	OpenAI audio API `style=cheerful`
Voice cloning	<30 s reference audio → TTS	Cloning opt-in flag	Azure Custom Neural Voice
Real-time streaming	Partial chunks	Play-while-generate	TTS `streaming=true`

Latency Benchmarks (2025)

Pipeline	P50 Latency	Notes
On-device ASR (mobile)	80 ms for 5-second utterance	Qualcomm SAI 2.0
Cloud ASR (large model)	150 ms	Whisper large v3
Neural TTS batch	200 ms for 20 tokens	GPU A100
Neural TTS streaming	<50 ms frame delay	Chunk size 40 ms

Design Trade-offs

Higher-quality voices need larger vocoders, increasing latency.
Streaming synthesis improves responsiveness but limits complex prosody planning.
Voice cloning raises privacy concerns; require explicit speaker consent.

Current Trends (2025)

Multilingual ASR models reach 30 ms per second of audio on edge NPUs.
Emotion-aware TTS adds controllable expressiveness knobs (style tokens).
End-to-end direct speech-to-speech translation models emerge, bypassing intermediate text.

Implementation Tips

Cache frequent system messages as pre-rendered audio.
Insert dynamic silence trimming to avoid audible gaps in streaming mode.
Normalize sample rates to 16 kHz PCM before feeding ASR pipeline.
Comply with local laws on synthetic voice disclosure (e.g., SB 912 in California).