Speech Generation Evaluation

Speech generation evaluation asks whether synthesized speech says the right words, sounds natural, preserves speaker identity, and follows instructions about prosody, emotion, or conversational style. Approaches include ASR-based WER and CER, acoustic and MOS-prediction metrics, speaker and emotion embeddings, human listening tests, and pairwise preference models or large audio-language model judges. Benchmarks increasingly test multilingual and domain-specific TTS, fine-grained paralinguistic control, nonverbal vocalizations, and timing and turn-taking in spoken dialogue. A central tension is that automatic scores can miss prosodic, linguistic, or contextual errors that people notice, while gains in intelligibility or semantic alignment may come at the expense of voice identity, expressiveness, or conversational naturalness.