Multi-speaker text-to-speech (TTS) systems generate speech in different voices by incorporating speaker-specific information during training and synthesis. Unlike single-speaker models, which learn one voice, these systems use datasets containing recordings from multiple speakers. During training, the model learns to associate linguistic features (like phonemes or prosody) with unique vocal characteristics (such as pitch or timbre) for each speaker. At inference time, the system combines the input text with a speaker identifier or embedding to produce speech in the target voice. This allows a single model to output diverse voices without requiring separate models per speaker.
The core architecture typically includes a speaker embedding layer, which encodes vocal traits into a numerical vector. These embeddings are either learned during training (tied to speaker IDs) or extracted from reference audio using a separate encoder. For example, in models like Tacotron 2 or FastSpeech 2, the speaker embedding is concatenated with linguistic features at each processing step, guiding the acoustic model to adjust pitch, rhythm, and tone. Training involves optimizing the model to minimize reconstruction loss across all speakers, forcing it to disentangle shared linguistic patterns from speaker-specific traits. Some systems also use adversarial training or variational autoencoders to better separate speaker identity from content.
Advanced systems support zero-shot voice cloning, where a short audio sample of an unseen speaker is used to generate their voice. This relies on a pre-trained speaker encoder that generalizes across voices, creating embeddings from brief clips. For instance, models like YourTTS or VITS can mimic new speakers with minimal data by combining a base TTS model with this encoder. Challenges include maintaining voice consistency for longer sentences and avoiding overfitting to dominant speakers in the dataset. Practical applications range from audiobooks with multiple narrators to personalized voice assistants, though ethical considerations around voice cloning remain critical.