Transcription correction using multi-token structures
A marking and marking technology, applied in speech analysis, speech recognition, instruments, etc., can solve problems such as imperfect speech recognition
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment Construction
[0014] A method of correcting speech recognition errors may use a word confusion network that may provide alternatives for certain words once the user indicates that a hypothesis (eg, result) provided to the user is not what the user wants. However, in general, word confusion networks (WCNs) do not address the problem of alternatives or corrections across multiple words or nodes of a WCN. An additional challenge comes from the fact that speech recognition occurs at the lexical level, and thus WCNs are generated at the lexical level, where the text presented to the user contains tokens as a result of text normalization on the lexical output. Thus, common WCNs may struggle to handle corrections in the presence of altered words associated with spoken utterances.
[0015] Examples of the present disclosure describe the generation of multi-arc token-level confusion networks that represent hypotheses for recognition results of spoken utterances to improve the ability to return to us...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 


