Building speech models for new words in a multi-word utterance

• US 5,765,132 A
• Filed: 10/26/1995
• Issued: 06/09/1998
• Est. Priority Date: 10/26/1995
• Status: Expired due to Term

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Dragon Systems, Inc. (Microsoft Corporation)
• Inventors: Roberts, Jed M.
• Primary Examiner(s): Hudspeth, David R.
• Assistant Examiner(s): Smits, Talivaldis Ivars
• Application Number:
  US08/548,409
• Time in Patent Office: 957 Days
• Field of Search: 395/2.62, 395/2.63, 395/2.64, 704/243, 704/253, 704/254, 704/270
• US Class Current: 704/254
• CPC Class Codes: G10L 15/063 Training

Abstract

Words are added to a speech recognition system vocabulary during user dictation by (a) extracting, from a multi-word user utterance, speech frames that correspond to each one of the one or more new words; and (b) building speech models for the one or more new words using the extracted speech frames.

Claims

1. A method for dynamically adding new words to a speech recognition system vocabulary during continuous speech dictation, comprising:

determining whether a multi-word user utterance contains one or more new words not in the speech recognition system vocabulary;

extracting speech frames from the multi-word user utterance that correspond to the one or more new words not in the speech recognition system vocabulary; and

building speech models for each of the one or more new words using the extracted speech frames.

2. The method of claim 1 further comprising:

saving of the speech models in the speech recognition system vocabulary.

3. The method of claim 2 further comprising:

before the extracting step, providing a correct spelling of each of the one or more new words, wherein the saving step includes saving the correct spelling of each of the one or more new words in the speech recognition system vocabulary.

4. The method of claim 1 wherein the extracting step is an iterative process including:

aligning speech frames of the multi-word user utterance against speech elements of the multi-word user utterance;

building new speech models for the one or more new words in accordance with the alignment;

re-aligning the speech frames of the multi-word user utterance using the new speech models;

comparing the re-alignment to the alignment; and

if the re-alignment and the alignment are not the same, repeating the building step, in accordance with the re-alignment, and the re-aligning and comparing steps until the re-alignment and the previous alignment are the same or for a predetermined number of times.

5. The method of claim 4, wherein the speech elements comprise syllable approximations.

6. The method of claim 5, wherein the aligning step further comprises:

assigning the syllable approximations to words in proportion to a length of each word.

7. The method of claim 6 wherein the length of each word comprises the number of letters in the word.

8. The method of claim 6 wherein the length of each word comprises the number of vowels in the word.

9. The method of claim 5 wherein determining syllable approximations includes:

determining syllable boundaries including;

detecting a first speech boundary speech frame in the multi-word user utterance as the first speech boundary speech frame having an amplitude that exceeds a speech trigger;

detecting a first silence boundary speech frame as the first speech frame, prior to the first speech boundary speech frame, to have an amplitude that exceeds a silence trigger, wherein the first silence boundary speech frame comprises a first syllable boundary;

detecting a second silence boundary speech frame as the first speech frame, after the first speech boundary speech frame, to have an amplitude that falls below the silence trigger; and

detecting local minima between the first and second silence boundary speech frames, wherein the local minima and second silence boundary speech frames comprise next syllable boundaries.

10. The method of claim 9 wherein determining syllable boundaries further includes:

repeating the steps of claim 10 until each speech frame in the multi-word user utterance has been considered.

11. The method of claim 10 further including:

determining whether the number of syllable approximations is less than the number of words in the multi-word user utterance; and

if so, dividing a syllable approximation corresponding to a largest number of speech frames into multiple syllable approximations.

12. The method of claim 11 further including:

repeating the steps of claim 12 until the number of syllable approximations is greater than or equal to the number of words in the multi-word user utterance.

13. The method of claim 4, wherein the aligning step comprises:

prompting the user for discrete utterances of each of the one or more new words;

building new speech models for the one or more new words using the discrete utterances; and

aligning the speech frames of the multi-word user utterance using the new speech models.