To utilize learned speaker embeddings in the context of text-to-speech (TTS) models, the following steps are typically followed: 1. Learning Speaker Embeddings: Large datasets of audio recordings are annotated with speaker labels and are used to train models for learning speaker embeddings. These embeddings capture the unique characteristics and attributes of individual speakers, allowing the TTS system to generate speech with speaker-specific qualities. 2. Dataset Preparation: The training dataset consists of audio recordings associated with their corresponding speaker labels. The audio may contain various sentences or utterances spoken by different speakers. The speaker labels are crucial for associating the audio with specific individuals during the training process. 3. Training with Speaker-Labelled Audio: The TTS model is trained using the large dataset of speaker-labelled audio. The model learns to extract meaningful representations from the audio input, including linguistic content and speaker-related characteristics. The speaker embeddings are computed or extracted from the audio using appropriate techniques, such as deep neural networks or speaker recognition models. 4. Integration of Speaker Embeddings: During training and inference time, the learned speaker embeddings are fed as input to the TTS model. The embeddings, which encapsulate the speaker-related information, are usually kept frozen or fixed while training the TTS model. This allows the model to focus on learning the linguistic aspects of speech synthesis while leveraging the pre-learned speaker embeddings. 5. Speaker-Adaptive Synthesis: By incorporating the speaker embeddings into the TTS model, the synthesized speech can exhibit speaker-specific qualities, such as accent, pitch, intonation, and timbre. During inference, when generating speech for a specific text, the corresponding speaker embedding is utilized to ensure the synthesized speech aligns with the desired speaker's characteristics. In summary, the process of utilizing learned speaker embeddings in TTS involves training models on speaker-labeled audio, extracting fixed embeddings, and incorporating these embeddings into the TTS system to generate speech with personalized or speaker-specific attributes. This approach enables the TTS system to produce speech that emulates the voice qualities of specific individuals present in the training data. ![[jia2018-speaker-encoder.png]] [Jia et al. (2018)](https://arxiv.org/pdf/1806.04558.pdf) [Audio samples (Jia et al., 2018)](https://google.github.io/tacotron/publications/speaker_adaptation/index.html)