A speech prediction model based on codec modeling and transformer decoding.

Area of Science:

• Signal Processing
• Machine Learning
• Speech Technology

Background:

• Speech prediction is crucial for real-time communication systems, particularly for packet loss concealment (PLC) and algorithmic delay compensation.
• Existing methods often rely on auxiliary information or lack the accuracy needed for high-quality speech reconstruction.

Purpose of the Study:

• To propose and evaluate a novel autoregressive speech prediction algorithm.
• To demonstrate the algorithm's effectiveness in packet loss concealment and frame-wise speech prediction tasks.
• To compare the proposed method against state-of-the-art baselines.

Main Methods:

• The proposed algorithm utilizes a speech codec and a transformer decoder for autoregressive prediction of missing speech frames.
• The model operates solely on speech data, eliminating the need for auxiliary information.
• A comparative study was conducted on packet loss concealment and frame-wise prediction tasks.

Main Results:

• The novel speech prediction model significantly outperforms existing state-of-the-art methods in both packet loss concealment and frame-wise prediction.
• Experimental results show substantial improvements compared to baselines, including on a recent packet loss concealment challenge.
• Systematic analysis identified key factors influencing prediction performance, such as context window and prediction lengths.

Conclusions:

• The proposed speech prediction algorithm offers a significant advancement in the field.
• The method provides superior prediction accuracy for tasks like packet loss concealment.
• The approach is robust and adaptable, with performance influenced by configurable parameters.