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Voice recognition by Mind Map: Voice recognition

1. Forensic voice identification

1.1. Defintion

1.1.1. (According to the wikipedia)Is the identification of the person who is speaking by characteristics of their voices (voice biometrics).

1.2. Principle

1.2.1. Every voice is individually characteristic enough to distinguish it from others through voiceprint analysis.

1.3. Advantages

1.3.1. Voice is unique, it is hard to simulate(It's quite safe).

1.3.2. It is easy to use.

1.4. Applications

1.4.1. Identification Identify the criminals As a ID card

1.4.2. Security USB drive Voice password Door Lock

1.5. Limitation

1.5.1. The technology are still developing(The result is not guaranteed).

1.5.2. Individual's voice can vary greatly in different situations.

1.5.3. It is not available to dumb people.

1.5.4. voice can be simple to tape. Hence, strangers can get your voice sample very easily.

1.5.5. Noise comes from the environment may affect the result(quite sensitive to the noise).

1.6. Useful Links



2. Differences between voice identification & speech recognition

3. Future Uses

3.1. Voice-activated assistants

3.2. intellectual control

3.2.1. Cars, robots and other electronic appliances

4. Background & History

4.1. Forensic voice identification

5. Speech recognition

5.1. Definition: determining what is being said

5.2. examples & applications

5.2.1. Audio search Music recognition Limitations Principle of design Applications/software Benefits

5.2.2. Speech-to-text Demostration

5.3. Principle of design

5.3.1. Speech-to-data analog-to-digital converter (ADC)

5.4. Benefits in daily use

5.4.1. People with disabilities (visually or lost his hands) can type in words to computer on their own

5.4.2. Drivers can dial without using their driving hands while driving

5.5. Two categories

5.5.1. Small-vocabulary/many-users Usage ideal for automated telephone answering Limitations limited to a small amount of pre-installed commands, such as basic menu options or numbers. Rewarding The system can always recognize the command precisely

5.5.2. Large-vocabulary/limited-users Usage work best in a business environment where only a small number of users use it Limitations The systems need to be trained first( to recognize the user's voice only works with a small amount of users accuracy rate will fall drastically with any other user Rewarding vocabularies in the tens of thousands good degree of accuracy (85 percent or higher with an expert user)

5.6. Weaknesses and Flaws

5.6.1. Low signal-to-noise ratio Causes Low-quality sound cards Works in a noisy contex Noise will interfere the waves which the system is going to recognize

5.6.2. Overlapping speech Current systems have difficulty separating simultaneous speech from multiple users

5.6.3. Homonyms Definition: two words that are spelled differently and have different meanings but sound the same. CAN'T tell the difference between these words based on sound alone examples of homonyms: extensive training of systems can improve the performance