Context-aware neural confidence estimation for rare word speech recognition
US12424206B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 23, 2023 |
| Grant date | Sep 23, 2025 |
| Priority date | — |
| Expiry date | Apr 9, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/06
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An automatic speech recognition (ASR) system that includes an ASR model, a neural associative memory (NAM) biasing model, and a confidence estimation model (CEM). The ASR model includes an audio encoder configured to encode a sequence of audio frames characterizing a spoken utterance into a sequence of higher-order feature representations, and a decoder configured to receive the sequence of higher-order feature representations and output a final speech recognition result. The NAM biasing model is configured to receive biasing contextual information and modify the sequence of higher-order feature representations based on the biasing contextual information to generate, as output, biasing context vectors. The CEM is configured to compute a confidence of the final speech recognition result output by the decoder. The CEM is connected to the biasing context vectors generated by the NAM biasing model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.