# US 2014/0207457 A1 · False alarm reduction in speech recognition systems using contextual information

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- Number: US 2014/0207457 A1
- Document ID: US20140207457A1
- Status: Published application
- Kind: A1
- Application number: 13/746,687
- Filed: 2013-01-22
- Published: 2014-07-24
- Earliest claimed: 2013-01-22
- Assignees: Interactive Intelligence, Inc.
- Applicants: Interactive Intelligence, Inc.
- Inventors: Konstantin Biatov; Aravind Ganapathiraju; Felix Immanuel Wyss
- Web page: https://patentgrep.com/patent/US20140207457A1

## CPC

- G10L 15/063: {Training}
- G10L 15/183: using context dependencies, e.g. language models
- G10L 2015/088: {Word spotting}
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

A system and method are presented for using spoken word verification to reduce false alarms by exploiting global and local contexts on a lexical level, a phoneme level, and on an acoustical level. The reduction of false alarms may occur through a process that determines whether a word has been detected or if it is a false alarm. Training examples are used to generate models of internal and external contexts which are compared to test word examples. The word may be accepted or rejected based on comparison results. Comparison may be performed either at the end of the process or at multiple steps of the process to determine whether the word is rejected.

## Claims

### Claim 1

A computer-implemented method of reducing false alarms in a speech recognition system, the method comprising the steps of:
a. analyzing a context of a set of words;
b. obtaining contexts for said words;
c. obtaining a set of training examples for said words;
d. generating a set of models of the contexts;
e. receiving a set of test words;
f. comparing said set of test words with said set of models;
g. obtaining a threshold for model comparison;
h. determining if a result of comparing said set of test words with a first one of the set of models is within the threshold; and
i. rejecting a word if the result is not within the threshold.

### Claim 2 (depends on 1)

The method of claim 1, wherein step (b) comprises defining a size of a local external context and a size of a local internal context of said words.

### Claim 3 (depends on 1)

The method of claim 1, wherein step (a) comprises the additional step of defining words from one of: a grammar specification, a vocabulary, and a set of user defined keywords.

### Claim 4 (depends on 1)

The method of claim 1, wherein the method is performed at an acoustical level.

### Claim 5 (depends on 1)

The method of claim 1, wherein the method is performed at a phonetic level.

### Claim 6 (depends on 1)

The method of claim 1, wherein the comparing of step (f) comprises the additional step of evaluating with a perplexity test a matching test word and a matching model.

### Claim 7 (depends on 1)

The method of claim 1, wherein the method is performed at a lexical level.

### Claim 8 (depends on 1)

The method of claim 1, wherein step (d) further comprises using sparse vectors to generate said models.

### Claim 9 (depends on 1)

The method of claim 1, further comprising the step of evaluating a word match using a distance metric.

### Claim 10 (depends on 9)

The distance metric of claim 9, wherein said distance metric comprises cosine distance.

### Claim 11

A computer-implemented method of reducing false alarms in a speech recognition system, the method comprising the steps of:
a. analyzing a context of a set of words;
b. obtaining contexts for said words;
c. obtaining a set of training examples for said words;
d. generating a set of models of the contexts;
e. receiving a set of test words;
f. comparing said set of test words with a first one of the set of models;
g. obtaining a threshold for model comparison;
h. determining if a result of comparing said set of test words with a first one of the set of models is within the threshold for said first model;
i. rejecting a word if the result is not within the threshold;
j. determining if a result of comparing said set of test words with a second one of the set of models is within a threshold for said second model; and
k. rejecting a word if the result does not meet the second model threshold.

### Claim 12 (depends on 11)

The method of claim 11 wherein step (b) comprises the additional step of defining a size of a local external context and a size of a local internal context of said words.

### Claim 13 (depends on 11)

The method of claim 11, wherein the method is performed at an acoustical level.

### Claim 14 (depends on 11)

The method of claim 11, wherein the method is performed at a phonetic level.

### Claim 15 (depends on 11)

The method of claim 11, wherein the comparing of step (f) comprises the additional step of evaluating the matches with a perplexity test.

### Claim 16 (depends on 11)

The method of claim 11, wherein step (a) comprises the additional step of analyzing a global context of a set of words.

### Claim 17 (depends on 16)

The method of claim 16, wherein the method is performed at a lexical level.

### Claim 18 (depends on 16)

The method of claim 16, wherein step (d) further comprises using sparse vectors to generate said models.

### Claim 19 (depends on 16)

The method of claim 16 further comprising the step of evaluating a word match using a distance metric.

### Claim 20 (depends on 19)

The distance metric of claim 19, wherein said distance metric comprises cosine distance.

### Claim 21

A computer-implemented method of reducing false alarms in a speech recognition system, the method comprising the steps of:
a. receiving training examples;
b. generating models of acoustical contexts of the training examples;
c. generating models of phonetic contexts of the training examples; and
d. generating models of linguistic contexts of the training examples.

### Claim 22 (depends on 21)

The method of claim 21, further comprising the steps of:
a. receiving a set of test examples of words;
b. comparing said test examples of words with said models of acoustical contexts;
c. determining if a result of comparing said test examples of words with said models of acoustical contexts is within a first threshold;
d. comparing said test examples of words with said models of phonetic contexts;
e. determining if a result of comparing said test examples of words with said models of phonetic contexts is within a second threshold;
f. comparing said test examples of words with said models of linguistic contexts; and
g. determining if a result of comparing said test examples of words with said models of linguistic contexts is within a third threshold.

### Claim 23 (depends on 21)

The method of claim 21 further comprising the steps of:
a. receiving a set of test examples of words;
b. comparing said test examples of words with at least one of said models of acoustical contexts, phonetic contexts, and linguistic contexts; and
c. determining if a result of comparing said test examples of words with said models of contexts is within a threshold.

### Claim 24 (depends on 22)

The method of claim 22 further comprising the additional step of accepting a word if a result of comparing meets a threshold for steps (c), (e), and (g).

### Claim 25 (depends on 22)

The method of claim 22 further comprising the additional step of rejecting a word if a result of comparing does not meet a threshold for steps (c), (e), and (g).

### Claim 26

A system for reducing false alarms in a speech recognition system comprising:
a. means for analyzing a context of a set of words;
b. means for obtaining contexts for said words;
c. means for obtaining a set of training examples for said words;
d. means for generating a set of models of the contexts;
e. means for receiving a set of test words;
f. means for comparing said set of test words with said set of models;
g. means for obtaining a threshold for model comparison;
h. means for determining if a result of comparing said set of test words with a first one of the set of models is within the threshold; and
i. means for rejecting a word if the result is not within the threshold.

### Claim 27 (depends on 26)

The system of claim 26 wherein the system additionally comprises:
a. means for testing the word against subsequent models; and
b. means for deciding to accept or reject the word after each test.

### Claim 28 (depends on 26)

The system of claim 26 wherein the means for generating a set of models comprises a statistical language model which defines a vocabulary from a word lexicon.

### Claim 29 (depends on 28)

The system of claim 28 wherein the statistical language model may comprise one of: a word grammar, a set of keywords, and a statistical n-gram language model.