Evaluating Google’s multi-speaker TTS: A case study in why private evaluations matter

As text-to-speech systems expand from single-voice narration into multi-speaker dialogue, evaluation must measure not only transcription accuracy but also speaker distinction, vocal consistency, natural transitions, conversational timing, and independent emotional expression. Researchers created 48 two-speaker scripts covering similar voices, interruptions, contrasting emotions, and extended conversations, then used blinded human raters to assess five Gemini TTS models on five corresponding dimensions. Gemini models generally performed well in emotional containment, voice stability, and seamlessness, while speaker separation remained the principal weakness, particularly for same-gender pairs; mixed-gender pairs scored much higher, and male-male pairs were often most difficult. Gemini 3.8 Flash showed substantial gains in speaker separation over earlier Flash models, though other models slightly exceeded it in seamlessness, illustrating trade-offs hidden by overall scores. Comparisons with ElevenLabs, Higgs Audio, and VibeVoice indicated that same-gender voice separation is a broader industry challenge, while systems can vary markedly between separating voices and creating convincing conversational dynamics. The evaluation framework is intended to help developers identify specific weaknesses, track progress across model generations, and design more targeted improvements for emerging voice-AI capabilities.

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