# US 8,543,393 B2 · Systems and methods of improving automated speech recognition accuracy using statistical analysis of search terms

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 8,543,393 B2
- Document ID: US8543393B2
- Status: Granted
- Kind: B2
- Application number: 12/124,060
- Filed: 2008-05-20
- Published: 2009-11-26
- Granted: 2013-09-24
- Earliest claimed: 2008-05-20
- Assignees: Calabrio, Inc.
- Applicants: David M. Barnish
- Inventors: David M. Barnish
- Web page: https://patentgrep.com/patent/US8543393B2

## CPC

- G10L 15/05: Word boundary detection
- G10L 15/01: Assessment or evaluation of speech recognition systems
- G10L 2015/025: {Phonemes, fenemes or fenones being the recognition units}
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

Systems and methods of improving speech recognition accuracy using statistical analysis of word or phrase-based search terms are disclosed. An illustrative system for statistically analyzing search terms includes an interface adapted to receive a text-based search term, a textual-linguistic analysis module that detects textual features within the search term and generates a first score, a phonetic conversion module that converts the search term into a phoneme string, a phonetic-linguistic analysis module that detects phonemic features within the phoneme string and generates a second score, and a score normalization module that normalizes the first and second scores and outputs a search term score to a user or process.

## Claims

### Claim 1

A system of statistically analyzing search terms for detection by a speech recognition engine, comprising
an interface adapted to receive a text-based search term;
a textual-linguistic analysis module adapted to detect one or more textual features within the search term and generate a first numeric score associated with the search term, the first numeric score comprising an unbounded score representing the sum of all textual features contained within the search term;
a phonetic conversion module adapted to convert the text-based search term into a phoneme string;
a phonemic-linguistic analysis module adapted to detect one or more phonemic features within the phoneme string and generate a second numeric score associated with the search term, the second numeric score comprising an unbounded score representing the sum of all phonemic features contained within the phoneme string;
a score normalization module adapted to normalize the first and second numeric scores and output a numeric search term score to a user or process, wherein the numeric search term score correlates with a probability that the search term will be accurately identified by a speech recognition engine.

### Claim 2 (depends on 1)

The system of claim 1, wherein the phonetic conversion module includes a language model dictionary and a phonetic alphabet.

### Claim 3 (depends on 2)

The system of claim 2, further including a phonetic generation algorithm or routine adapted to generate a phoneme string for phonemes not contained in the language model dictionary.

### Claim 4 (depends on 1)

The system of claim 1, wherein the textual-linguistic analysis module includes a table of textual features and associated weighting factors.

### Claim 5 (depends on 4)

The system of claim 4, wherein each textual feature within the search term is adjusted by a corresponding weighting factor.

### Claim 6 (depends on 1)

The system of claim 1, wherein the phonemic-linguistic analysis module includes a table of phonemic features and associated weighting factors.

### Claim 7 (depends on 6)

The system of claim 6, wherein each phoneme feature within the phoneme string is adjusted by a corresponding weighting factor.

### Claim 8 (depends on 1)

The system of claim 1, wherein the search term score is a whole number score.

### Claim 9 (depends on 1)

The system of claim 1, wherein the system is a computer-assisted system.

### Claim 10

A method of statistically analyzing search terms for detection by a speech recognition engine, comprising:
receiving a text-based search term from a user or process;
analyzing one or more textual features within the search term using a textual-linguistic analysis module;
computing a first numeric score associated with the one or more textual features within the search term, the first numeric score comprising an unbounded score representing the sum of all textual features contained within the search term;
converting the search term into a phoneme string and analyzing one or more phonemic features within the phoneme string using a phonemic-linguistic analysis module;
computing a second numeric score associated with the one or more phonemic features within the phoneme string, the second numeric score comprising an unbounded score representing the sum of all phonemic features contained within the phoneme string; and
normalizing the first and second numeric scores and outputting a numeric search term score to the user or process, wherein the numeric search term score correlates with a probability that the search term will be accurately identified by a speech recognition engine.

### Claim 11 (depends on 10)

The method of claim 10, wherein computing a first score associated with the one or more textual features within the search term includes applying weighting factors to each textual feature.

### Claim 12 (depends on 10)

The method of claim 10, wherein the first score correlates to the likelihood that the text of the search term will be accurately detected by a speech recognition engine.

### Claim 13 (depends on 10)

The method of claim 10, wherein computing a second score associated with the one or more phonemic features within the phoneme string includes applying weighting factors to each phonemic feature.

### Claim 14 (depends on 10)

The method of claim 10, wherein the second score correlates to the likelihood that the phoneme string will be accurately detected by a speech recognition engine.

### Claim 15 (depends on 10)

The method of claim 10, wherein converting the search term into a phoneme string is accomplished using a phonetic conversion module and a language model dictionary.

### Claim 16 (depends on 10)

The method of claim 10, wherein normalizing the first and second scores includes applying normalization constants to the first and second scores.

### Claim 17 (depends on 10)

The method of claim 10, wherein the method is a computer-assisted method executable on a computer-readable medium.

### Claim 18

A method of statistically analyzing search terms for detection by a speech recognition engine, comprising:
providing a text-based search term to a search-term analysis module including a textual-linguistic analysis module and a phonemic-linguistic analysis module;
performing a textual-linguistic analysis of the search term and generating a first numeric score based on one or more textual features found in the search term, the first numeric score representing the sum of all textual features contained within the search term;
converting the search term into a phoneme string;
performing a phonemic-linguistic analysis of the search term and generating a second numeric score based on one or more phonemic features found in the phoneme string, the second numeric score comprising an unbounded score representing the sum of all phonemic features contained within the phoneme string;
normalizing the first and second numeric scores and outputting a search term score correlating with a probability that the search term will be accurately identified by a speech recognition engine;
comparing the search term score against a threshold search term score, the threshold search term score corresponding to a threshold probability of identifying speech within a search term; and
outputting the search term to a database if the search term score is at or above the threshold search term score.