RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION

Smart overview of the Invention

The patent application introduces a method for enhancing text-to-speech (TTS) models using residual adapters to achieve few-shot speaker adaptation. This approach involves augmenting a pre-trained TTS model with a stack of residual adapters, allowing it to synthesize speech in the voice of a target speaker with minimal training data. The method focuses on optimizing these adapters while keeping the original TTS model parameters unchanged, enabling efficient adaptation to new speakers.

Technical Implementation

The method involves obtaining a TTS model pre-trained on a general dataset, which is then augmented with residual adapters. These adapters are integrated into the model's architecture, particularly within the decoder's multi-head self-attention layers. The model is then adapted using a small dataset of spoken utterances from a target speaker, paired with corresponding text transcriptions, to learn the speaker's voice characteristics.

Optimization Process

During adaptation, the residual adapters are optimized by generating synthetic speech representations from the input text and comparing them to the actual utterances of the target speaker. The loss calculated from this comparison guides the adjustment of the adapters' parameters. This process may also involve optimizing a speaker embedding specific to the target speaker, enhancing the model's ability to mimic their voice accurately.

Model Architecture

The TTS model can include components such as a BERT encoder for text processing, a variance adapter for stress and speaker embeddings, and a duration adapter for phoneme timing. Additionally, an upsampler and a decoder work together to produce the final synthetic speech output. The architecture ensures that the model can handle various input features and produce natural-sounding speech.

Language and Data Considerations

The initial training dataset used for pre-training the TTS model may not contain the target speaker's voice, allowing the model to adapt to new speakers without prior exposure. Furthermore, the model is capable of handling input text in different languages, facilitating cross-linguistic speaker adaptation. This flexibility is crucial for applications requiring multilingual support and rapid adaptation to diverse speaker profiles.