SSL (self-supervised learning) plays a key role in improving speech recognition and synthesis systems by enabling models to learn meaningful representations from raw audio data without requiring extensive labeled datasets. Instead of relying solely on transcribed speech for training, SSL models are pretrained on large amounts of unlabeled audio, allowing them to capture patterns like phonemes, intonation, and contextual relationships. These pretrained models can then be fine-tuned for specific tasks, such as converting speech to text or generating natural-sounding synthetic speech, with far less labeled data than traditional methods.
In speech recognition, SSL helps models generalize better to diverse accents, background noise, and speaking styles. For example, models like wav2vec 2.0 use SSL by masking parts of raw audio waveforms and training the model to predict the masked segments. This forces the model to learn robust acoustic features (e.g., distinguishing between similar-sounding words) and contextual dependencies (e.g., how words fit into phrases). When fine-tuned on a smaller labeled dataset, these pretrained models achieve higher accuracy with fewer training examples compared to systems trained from scratch. SSL also reduces reliance on handcrafted features like spectrograms, as models can directly process raw audio.
For speech synthesis, SSL enables systems to generate more natural and expressive voices by learning nuances like rhythm, emotion, and speaker identity from unlabeled data. For instance, models can be pretrained to reconstruct audio segments or predict prosodic features (e.g., pitch and duration) from a large corpus of diverse speech. This allows synthesis systems to mimic specific speaker styles with minimal data—useful for applications like voice cloning. Additionally, SSL helps disentangle linguistic content from acoustic variations, making it easier to control synthesized speech attributes (e.g., adjusting emotion without altering wording). By leveraging SSL, both recognition and synthesis systems become more adaptable, efficient, and scalable across languages and use cases.