ESPnet: End-to-end speech processing toolkit

Shinji Watanabe (CMU): ESPnet - End-to-end speech processing toolkit

An end-to-end neural approach has become a popular alternative to conventional modular approaches in various speech applications, including speech recognition and synthesis. One of the benefits of this end-to-end neural framework is that we can use a unified framework for different speech processing problems based on sequence-to-sequence modeling and tightly integrate these problems in a joint training manner. This talk introduces various end-to-end speech processing applications by focusing on the above-unified framework and several integrated systems (e.g., speech recognition and synthesis, speech separation and recognition, speech recognition and translation) implemented within an open-source toolkit named ESPnet (end-to-end speech processing toolkit https://github.com/espnet/espnet).