Document transcription system training

Patent metadata

Field	Value
Publication number	US-9552809-B2
Application number	US-201615066677-A
Country	US
Kind code	B2
Filing date	Mar 10, 2016
Priority date	Aug 20, 2004
Publication date	Jan 24, 2017
Grant date	Jan 24, 2017

Abstract

A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.

First claim

What is claimed is:

1. In a system including a first document containing at least some information in common with a spoken audio stream, a method comprising steps of:
(A) determining that text in the first document represents an instance of a concept, comprising determining that the text has a format associated with the concept;
(B) replacing the identified text with a context-free grammar specifying the plurality of spoken forms of the concept to produce a second document;
(C) generating a document-specific language model based on the second document, comprising generating at least some of the document-specific language model based on the context-free grammar;
(D) using the first language model in a speech recognition process to recognize the spoken audio stream and thereby to produce a third document.

2. The method of claim 1 , further comprising:
(E) using the third document and the spoken audio stream to train an acoustic model.

3. The method of claim 2 , wherein (E) comprises:
(E) (1) filtering text from the third document by reference to the second document to produce a filtered document in which text filtered from the third document is marked as unreliable; and
(E) (2) using the filtered document and the spoken audio stream to train the acoustic model.

4. A non-transitory computer-readable medium comprising computer program instructions executable by at least one computer processor to perform a method for use with a system, the system including a first document containing at least some information in common with a spoken audio stream, the method comprising:
(E) determining that text in the first document represents an instance of a concept, comprising determining that the text has a format associated with the concept;
(F) replacing the identified text with a context-free grammar specifying the plurality of spoken forms of the concept to produce a second document;
(G) generating a document-specific language model based on the second document, comprising generating at least some of the document-specific language model based on the context-free grammar;
(H) using the first language model in a speech recognition process to recognize the spoken audio stream and thereby to produce a third document.

5. The non-transitory computer-readable medium of claim 4 , wherein the method further comprises:
(E) using the third document and the spoken audio stream to train an acoustic model.

6. The non-transitory computer-readable medium of claim 5 , wherein (E) comprises:
(E) (1) filtering text from the third document by reference to the second document to produce a filtered document in which text filtered from the third document is marked as unreliable; and
(E) (2) using the filtered document and the spoken audio stream to train the acoustic model.

Assignees

• Mmodal Ip Llc

Inventors

• Yegnanarayanan Girija
• Finke Michael
• Fritsch Juergen
• Koll Detlef
• Woszczyna Monika

Classifications

• G10L15/063 Primary

  Training · CPC title

• G10L15/193

  Formal grammars, e.g. finite state automata, context free grammars or word networks · CPC title

• G10L15/26

  Speech to text systems (G10L15/08 takes precedence) · CPC title