Word spotting score normalization

• US 7,650,282 B1
• Filed: 07/22/2004
• Issued: 01/19/2010
• Est. Priority Date: 07/23/2003
• Status: Active Grant

• Current Assignee: Nexidia Incorporated (Nice Ltd)
• Original Assignee: Nexidia Incorporated (Nice Ltd)
• Inventors: Morris, Robert W.
• Primary Examiner(s): Smits; Talivaldis I
• Assistant Examiner(s): KOVACEK, DAVID M
• Application Number:
  US10/897,155
• Time in Patent Office: 2,007 Days
• Field of Search: 704/231, 704236-275, 704E17001-E17004, 704E17007-E17012, 704/E17.014, 704E15001-E1505, 455/415, 4555501-5531, 379 8801- 8828, 379 9301- 9312, 379 9326- 9337, 3794061-40616
• US Class Current: 704/256.1
• CPC Class Codes: G10L 15/187 Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
• CPC Class Codes: G10L 2015/088 Word spotting

Abstract

An approach to scoring acoustically-based events, such as hypothesized instances of keywords, in a speech processing system make use of scores of individual components of the event. Data characterizing an instance of an event are first accepted. This data includes a score for the event. The event is associated with a number of component events from a set of component events, such as a set of phonemes. Probability models are also accepted for component scores associated with each of the set of component events in each of two of more possible classes of the event, such as a class of true occurrences of the event and a class of false detections of the event. The event is then scored. This scoring includes computing a probability of one of the two or more possible classes for the event using the accepted probability models.

Claims

1. A method for processing acoustically-based events according to a predefined plurality of component events, each component event having a recognition model and having corresponding distributions of recognition scores resulting from application of the recognition model to acoustically-based events, the method comprising:

accepting data characterizing a detected instance of an acoustically-based event that is represented by a set of component events, said data including a first recognition score for said detected instance of the acoustically-based event;

accepting, for each recognition model of a component event, a plurality of distributions of recognition scores, each distribution of recognition scores for a recognition model being associated with a corresponding different class of a plurality of possible classes, the possible classes including at least a class of true occurrences; and

scoring the detected instance of the acoustically-based event, including computing a second recognition score for said detected instance of the acoustically-based event using (i) the accepted distributions of recognition scores for the set of component events used to represent the acoustically-based event, and (ii) the first recognition score for the acoustically-based event.

2. The method of claim 1 wherein the possible classes include false detections.

3. The method of claim 1 wherein the acoustically-based event includes a linguistically-defined event.

4. The method of claim 3 wherein the linguistically-defined event includes one or more word events.

5. The method of claim 4 wherein the component events include subword units.

6. The method of claim 5 wherein the subword units include phonemes.

7. The method of claim 1 further comprising accepting data characterizing a plurality of instances of acoustically-based events, at least some of the acoustically-based events being known to belong to each of the possible classes.

8. The method of claim 7 further comprising estimating parameters for the distributions of recognition scores from the data characterizing the plurality of instances of acoustically-based events.

9. The method of claim 8 wherein estimating the parameters includes applying a Gibbs sampling approach.

10. The method of claim 1 wherein scoring the detected instance of the acoustically-based event includes computing the second recognition score to characterize a degree to which the first recognition score is consistent with the distributions for the component events in the true occurrence class.

11. The method of claim 1 wherein scoring the detected instance of the acoustically-based event includes computing the second recognition score to characterize a probability that the detected instance of the acoustically-based event belongs to the true occurrence class.

12. A computer-readable medium comprising instructions for causing a computing system to perform operations for processing acoustically-based events according to a predefined plurality of component events, each component event having a recognition model and having corresponding distributions of recognition scores resulting from application of the recognition model to acoustically-based events, the operations including:

accepting data characterizing a detected instance of an acoustically-based event that is represented by a set of component events, said data including a first recognition score for said detected instance of the acoustically-based event;

accepting, for each recognition model of a component event, a plurality of distributions of recognition scores, each distribution of recognition scores for a recognition model being associated with a corresponding different class of a plurality of possible classes, the possible classes including at least a class of true occurrences; and

scoring the detected instance of the acoustically-based event, including computing a second recognition score for said detected instance of the acoustically-based event using (i) the accepted of recognition scores for the set of component events used to represent the acoustically-based event, and (ii) the first recognition score for the acoustically-based event.

13. A system for processing acoustically-based events according to a predefined plurality of component events, each component event having a recognition model and having corresponding distributions of recognition scores resulting from application of the recognition model to acoustically-based events, the system comprising:

a first input for accepting data characterizing a detected instance of an acoustically-based event that is represented by a set of component events, said data including a first recognition score for said detected instance of the acoustically-based event;

storage, for each recognition model of a component event, a plurality of distributions of recognition scores, each distribution of recognition scores for a recognition model being associated with a corresponding different class of a plurality of possible classes, the possible classes including at least a class of true occurrences; and

a computational component for computing a second recognition score for said detected instance of the acoustically-based event using (i) the accepted distributions for the set of component events used to represent the acoustically-based event, and (ii) the first recognition score for the acoustically-based event; and

an output for providing the second recognition score for the detected instance of the acoustically-based event.