Method and system for accent correction

• US 8,175,882 B2
• Filed: 01/25/2008
• Issued: 05/08/2012
• Est. Priority Date: 01/25/2008
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Basson, Sara H., Kelley, Edward E., Ramabhadran, Bhuvana, Kanevsky, Dimitiri
• Primary Examiner(s): Dorvil, Richemond
• Assistant Examiner(s): Borsetti, Greg
• Application Number:
  US12/019,709
• Publication Number:
  US 20090192798A1
• Time in Patent Office: 1,565 Days
• Field of Search: 704 9- 10, 704/234, 704/237, 704/239, 704/270, 704/E11.001, 434/156, 434/157, 434/167, 434/185
• US Class Current: 704/270
• CPC Class Codes: G06F 40/58 Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models

Abstract

A method for task execution improvement, the method includes: generating a baseline model for executing a task; recording a user executing a task; comparing the baseline model to the user'"'"'s execution of the task; and providing feedback to the user based on the differences in the user'"'"'s execution and the baseline model.

Claims

1. A method for accent correction, the method comprising:

training a first automatic speech recognition system (ASR1) with native speakers in a language;

training a second automatic speech recognition system (ASR2) with non-native speakers of the language;

receiving a spoken language sample from a user into ASR1 and ASR2 simultaneously, wherein the user was not one of the native speakers and the user was not one of the non-native speakers;

determining whether the received spoken language sample more closely approximates the output of ASR1 or ASR2; and

providing a feedback indicator to the user representative of their level of accent improvement, wherein the feedback indicator is based on a level of correlation and pattern matching between the received spoken language samples and ASR1 and ASR2.

2. The method of claim 1, wherein the output from ASR1 and ASR2 is at least one of phonetic units, words, or phrases.

3. The method of claim 1, wherein the accent correction is offered as a pay for use service.

4. The method of claim 3, wherein the pay for use service is available over the Internet.

5. A system for learning and task execution improvement, the system comprising:

a user dependent imitation machine that learns a user'"'"'s behaviors, actions and behaviors for carrying out a task;

a user independent machine that records defined and accepted behaviors, actions, and techniques for carrying out the task;

a comparator in electrical signal communication with the user dependent imitation machine, the user independent machine, and a learning module;

wherein the comparator determines differences in recorded actions, behaviors, and techniques between the user dependent imitation machine and the user independent machine;

wherein the learning module produces recommendations for correcting the user'"'"'s behaviors, actions, and techniques for executing the task in response to the differences determined by the comparator; and

wherein the learning module provides the recommendations as feedback to the user to improve execution of the task;

wherein the task is user accent correction;

wherein the user dependent imitation machine is a first automatic speech recognition system (ASR1) trained by non-native speakers in a language;

wherein the user independent machine is a second automatic speech recognition system (ASR2) trained with native speakers of the language, wherein the user was not one of the native speakers and the user was not one of the non-native speakers;

wherein the system;

receives a spoken language sample from a user into ASR1 and ASR2 simultaneously;

determines whether the received spoken language sample more closely approximates the output of ASR1 or ASR2; and

provides a feedback indicator to the user representative of their level of accent improvement from the learning module, wherein the feedback indicator is based on a level of correlation and pattern matching between the received spoken language samples and ASR1 and ASR2.

6. The system of claim 5, wherein the output from ASR1 and ASR2 is at least one of phonetic units, words, or phrases.

7. The system of claim 5, wherein the accent correction is offered as a pay for use service.

8. The system of claim 7, wherein the pay for use service is available over the Internet.