Dec 19, 2017 · We show that joint training improves relative performance by 4% to 13% for our end-to-end model as compared to the same model learned through ...

ABSTRACT. Connectionist temporal classification (CTC) is widely used for maximum likelihood learning in end-to-end speech recog- nition models.

A deep end-to-end speech recognition model is provided. Multi-objective learning criteria are used to train the model on training data comprising speech ...

It is shown that joint training improves relative performance by 4% to 13% for the end-to-end model as compared to the same model learned through maximum ...

We show that joint training improves relative performance by 4% to 13% for our end-to-end model as compared to the same model learned through maximum likelihood ...

Dec 14, 2017 · We show that the performance of the end-to-end speech models can be improved significantly by performing proper regularization and adjustment to the training ...

We propose keeping track of the decisions that the system has made, and using them to constrain the system's future behavior in the dialogue. In this way, we ...

Oct 9, 2023 · We propose Latent Synthesis (LaSyn), an efficient textual data utilization framework for E2E speech processing models.

A list of End-to-End speech recognition, including papers, codes and other materials - charlesliucn/awesome-end2end-speech-recognition.

Training endto-end (E2E) speech recognition models without careful attention to such data results in sub-optimal performance as models prioritize learning wake- ...