# US 5,710,864 A · Systems, methods and articles of manufacture for improving recognition confidence in hypothesized keywords

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- Number: US 5,710,864 A
- Document ID: US5710864A
- Status: Granted
- Kind: A
- Application number: 08/366,162
- Filed: 1994-12-29
- Granted: 1998-01-20
- Earliest claimed: 1994-12-29
- Assignees: Lucent Technologies Inc.
- Inventors: Biing-Hwang Juang; Chin-Hui Lee; Richard Cameron Rose
- Web page: https://patentgrep.com/patent/US5710864A

## CPC

- G10L 15/07: to the speaker
- G10L 15/142: {Hidden Markov Models [HMMs]}
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

Systems, methods and articles of manufacture are provided for adjusting the parameters of ones of a plurality of recognition models. The recognition models collectively represent a vocabulary. The recognition models are utilized to identify a known word represented within a received input signal. The received input signal may include within vocabulary and out of vocabulary words. An output signal representing a confidence measure corresponding to the relative accuracy of the identity of the known word is generated. Particular ones of the plurality of recognition models are adjusted as a function of the output signal to improve the confidence measure. The systems, methods and articles of manufacture are preferably implemented in accordance with discriminative techniques, and the adjustment process is used during either a preferred training or a recognition mode.

## Claims

### Claim 1

A speech recognition system comprising:
a memory for storing a plurality of recognition models wherein each one of said recognition models represents a known word within a vocabulary, said plurality of recognition models including keyword recognition models representing keywords and alternate hypothesis recognition models representing words phonetically similar to the keywords; and
a processor having an input for receiving an input signal representing a speech utterance, the processor processing said input signal utilizing ones of said plurality of recognition models to identify a particular known word represented within said input signal, generating an output signal representing a confidence measure corresponding to the relative accuracy of the identity of said particular known word, and adjusting at least one of said alternate hypothesis recognition models as a function of said output signal to improve said confidence measure.

### Claim 2 (depends on 1)

The system as set forth in claim 1 wherein said memory further operates to store a plurality of processing unit instructions.

### Claim 3 (depends on 2)

The system as set forth in claim 2 wherein said processor includes one or more processing units and is further operable to retrieve and execute selected ones of said processing unit instructions, said selected ones of said processing unit instructions directing said processor to process said input signal utilizing ones of said plurality of recognition models.

### Claim 4 (depends on 1)

The system as set forth in claim 1 wherein said processor utilizes discriminative techniques and wherein said processor further produces an indicator signal indicating that said particular known word is one of a particular keyword and a particular phonetically similar word.

### Claim 5 (depends on 4)

The system as set forth in claim 4 wherein said processor subtracts a measure of the probability that said particular known word is a keyword from a measure of the probability that said particular known word is a word phonetically similar to a keyword.

### Claim 6 (depends on 1)

The system as set forth in claim 1 wherein said memory includes a keyword recognition model database and an alternate hypothesis recognition model database.

### Claim 7 (depends on 1)

The system as set forth in claim 1 wherein said processor is operable during at least one of a training mode and a recognition mode.

### Claim 8 (depends on 1)

The system as set forth in claim 1 wherein said processor is further operative to produce one or more feature vectors characterizing said received input signal.

### Claim 9 (depends on 1)

The system as set forth in claim 1 wherein said input signal is representative of an unknown speech utterance.

### Claim 10 (depends on 9)

The system as set forth in claim 9 wherein said processor is further operable to produce an indicator signal having one of a first state indicating said confidence measure associated with said particular known word corresponds to particular threshold, and a second state indicating said confidence measure associated with said particular known word does not correspond to said particular threshold.

### Claim 11 (depends on 1)

The system as set forth in claim 1 wherein said plurality of recognition models includes one or more Hidden Markov Models.

### Claim 12

A method for improving the accuracy of a speech recognition system, said speech recognition system utilizing a plurality of recognition models to identify a particular known word within a received input signal, said received input signal representative of a speech utterance, each one of said recognition models representing different known words within a vocabulary, said plurality of recognition models including keyword recognition models representing keywords and alternate hypothesis recognition models representing words phonetically similar to the keywords, said method comprising the steps of:
identifying said particular known word utilizing ones of said plurality of recognition models;
generating an output signal representing a confidence measure corresponding to the relative accuracy of said identification step; and
adjusting at least one of said alternate hypotheses recognition models as a function of said output signal to improve said confidence measure.

### Claim 13 (depends on 12)

The method as set forth in claim 12 wherein said identification step further includes the step of utilizing discriminative techniques.

### Claim 14 (depends on 13)

The method as set forth in claim 13 further including the step of indicating whether said particular known word is one of a particular keyword and a particular phonetically similar word.

### Claim 15 (depends on 14)

The method as set forth in claim 14 wherein said adjusting step further includes the step of subtracting a measure of the probability that said particular known word is a keyword from a measure of the probability that said particular known word is a word phonetically similar to a keyword.

### Claim 16 (depends on 13)

The method as set forth in claim 13 further including the step of storing said keyword recognition models in a keyword recognition model database and said alternate hypothesis recognition models in an alternate hypothesis recognition model database.

### Claim 17 (depends on 12)

The method as set forth in claim 12 wherein said method operates during at least one of a training mode and a recognition mode.

### Claim 18 (depends on 12)

The method as set forth in claim 12 wherein said identification step is preceded by the step of producing one or more feature vectors characterizing said received input signal.

### Claim 19 (depends on 12)

The method as set forth in claim 12 wherein said input signal is representative of an unknown speech utterance, and said method further includes the step of producing an indicator signal having one of a first state indicating said confidence measure associated with said particular known word corresponds to a particular threshold, and a second state indicating said confidence measure associated with said particular known word does not correspond to said particular threshold.

### Claim 20 (depends on 12)

The method as set forth in claim 12 wherein said plurality of recognition models includes one or more Markov Hidden Models.

### Claim 21

A storage medium for storing a plurality of processing system instructions, said plurality of processing system instructions capable of being read and executed by one or more processing units for performing speech recognition, said memory storage device including:
a first instruction set for storing a plurality of recognition models wherein each one of said recognition models represents a different known word within a vocabulary, said plurality of recognition models including keyword recognition models representing keywords and alternate hypothesis recognition models representing words phonetically similar to the keywords;
a second instruction set for storing a received input signal representing a speech utterance;
a third instruction set for utilizing ones of said plurality of recognition models to identify a particular known word represented within said input signal, generating an output signal representing a confidence measure corresponding to the relative accuracy of the identity of said particular known word, and adjusting at least one of said alternate hypothesis recognition models as a function of said output signal to improve said confidence measure.

### Claim 22 (depends on 21)

The storage medium as set forth in claim 21 wherein said storage medium is selected from the group consisting of:
a magnetic device operable to utilize patterns of magnetism to store said processing system instructions,
a semiconductor chip operable to utilize on-off static electric charges to store said processing system instructions, and
an optical memory operable to utilize on-off light beams to store said processing system instructions.

### Claim 23 (depends on 21)

The storage medium as set forth in claim 21 wherein said third instruction set further utilizes discriminative techniques.

### Claim 24 (depends on 23)

The storage medium as set forth in claim 23 wherein said first instruction set stores said keyword recognition models to a keyword recognition model database and said alternate hypothesis recognition models to an alternate hypothesis recognition model database.

### Claim 25 (depends on 23)

The storage medium as set forth in claim 23 wherein said third instruction set further indicates whether said particular known word is one of a particular keyword and a particular phonetically similar word.

### Claim 26 (depends on 25)

The storage medium as set forth in claim 25 wherein said third instruction set subtracts a measure of the probability that said particular known word is a keyword from a measure of the probability that said particular known word is a word phonetically similar to a keyword.

### Claim 27 (depends on 21)

The storage medium as set forth in claim 21 wherein said third instruction set is operable during at least one of a training mode and a recognition mode.

### Claim 28 (depends on 21)

The storage medium as set forth in claim 21 wherein said second instruction set produces one or more feature vectors characterizing said received input signal.

### Claim 29 (depends on 21)

The storage medium as set forth in claim 21 wherein said input signal is representative of an unknown speech utterance, and said third instruction set produces an indicator signal having one of a first state indicating said confidence measure associated with said particular known word corresponds to a particular threshold, and a second state indicating said confidence measure associated with said particular known word does not correspond to said particular threshold.

### Claim 30 (depends on 21)

The storage medium as set forth in claim 21 wherein said plurality of recognition models include one or more Markov Hidden Models.