#llm/audio + #speech-to-text

• Gemini 3.8 Flash TTS: Built for deep creative direction and character design. Create entirely new voices from scratch using natural language prompts to bring characters to life across gaming, immersive audiobooks, podcasts, and interactive media. Direct every performance line by line with granular control over acting cues, pacing, dialect shifts, and backchanneling.
• Gemini 3.8 Flash-Lite TTS: Built for high-volume, cost-efficient scale. Optimized for high-volume dubbing, audio content creation, and expressive voice agents with fine-grained control over tone, pacing, and expressive nuance.

These models complement our fast-growing Gemini Audio family, following 3.5 Live Translate, 3.5 Transcribe, 3.8 Live, and 3.8 Live Extended Thinking.

NISQA is a deep learning model/framework for speech quality prediction. The NISQA model weights can be used to predict the quality of a speech sample that has been sent through a communication system (e.g telephone or video call). Besides overall speech quality, NISQA also provides predictions for the quality dimensions Noisiness, Coloration, Discontinuity, and Loudness to give more insight into the cause of the quality degradation.

One of Deepgram’s goals for the upcoming year is to pass the Audio Turing Test, which assesses how realistic and human-like AI-generated audio sounds.

Voxtral Realtime - A 4 billion parameter model aimed at live transcription, achieving “state of the art” transcription with 480ms latency across 13 languages. It can be configurable down to sub-200ms latency.

Performance on the FLEURS benchmark shows that Voxtral Mini Transcribe V2 performs competitively against models from Gemini and OpenAI, with the lowest diarization error rate.