User:
kmaclean
Date: 1/1/2010 2:10 pm
Views: 82919
Rating: 37
An acoustic model
is a file that contains statistical representations of each of the
distinct sounds that makes up a word. Each of these statistical
representations is assigned a label called a phoneme. The English language has about 40 distinct sounds that are useful for speech recognition, and thus we have 40 different phonemes.
An acoustic model is created by taking a large database of speech (called a speech corpus)
and using special training algorithms to create statistical
representations for each phoneme in a language. These statistical
representations are called Hidden Markov Models ("HMM"s). Each phoneme has its own HMM.
For example, if the system is set up with a simple grammar
file to recognize the word "house" (whose phonemes are: "hh aw s"),
here are the (simplified) steps that the speech recognition engine
might take:
- The speech decoder
listens for the distinct sounds spoken by a user and then looks for a
matching HMM in the Acoustic Model. In our example, each of the
phonemes in the word house has its own HMM:
- When it finds a matching HMM in the acoustic model,
the decoder takes note of the phoneme. The decoder keeps track of the
matching phonemes until it reaches a pause in the users speech.
- When a pause is reached, the decoder looks up the matching series
of phonemes it heard (i.e. "hh aw s") in its Pronunciation Dictionary
to determine which word was spoken. In our example, one of the entries
in the pronunciation dictionary is HOUSE:
- ...
- HOUSAND [HOUSAND] hh aw s ax n d
- HOUSDEN [HOUSDEN] hh aw s d ax n
- HOUSE [HOUSE] hh aw s
- HOUSE'S [HOUSE'S] hh aw s ix z
- HOUSEAL [HOUSEAL] hh aw s ax l
- HOUSEBOAT [HOUSEBOAT] hh aw s b ow t
- ...
- The decoder then looks in the Grammar file for a matching word or
phrase. Since our grammar in this example only contains one word
("HOUSE"), it returns the word "HOUSE" to the calling program.
This get a little more complicated when you start using Language Models
(which contain the probabilities of a large number of different word
sequences), but the basic approach is the same.