2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | HLT-1.4 | ||
| Paper Title | PERSONALIZATION STRATEGIES FOR END-TO-END SPEECH RECOGNITION SYSTEMS | ||
| Authors | Aditya Gourav, Linda Liu, Ankur Gandhe, Yile Gu, Guitang Lan, Xiangyang Huang, Shashank Kalmane, Gautam Tiwari, Denis Filimonov, Ariya Rastrow, Andreas Stolcke, Ivan Bulyko, Amazon, United States | ||
| Session | HLT-1: Language Modeling 1: Fusion and Training for End-to-End ASR | ||
| Location | Gather.Town | ||
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 | ||
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-LVCR] Large Vocabulary Continuous Recognition/Search | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | The recognition of personalized content, such as contact names, remains a challenging problem for end-to-end speech recognition systems. In this work, we demonstrate how first and second-pass rescoring strategies can be leveraged together to improve the recognition of such words. Following previous work, we use a shallow fusion approach to bias towards recognition of personalized content in the first-pass decoding. We show that such an approach can improve personalized content recognition by up to 16% with minimum degradation on the general use case. We describe a fast and scalable algorithm that enables our biasing models to remain at the word-level, while applying the biasing at the subword level. This has the advantage of not requiring the biasing models to be dependent on any subword symbol table. We also describe a novel second-pass de-biasing approach: used in conjunction with a first-pass shallow fusion that optimizes on oracle WER, we can achieve an additional 14% improvement on personalized content recognition, and even improve accuracy for the general use case by up to 2.5% | ||