02-03-2012, 03:20 PM
Speech Recognition System Based on Hidden Markov Models
[attachment=17891]
INTRODUCTION
The field of Automatic Speech Recognition (ASR) is about 60 years old.
First speech recognizer was invented at BELL LABS in 1950
Development of ASR increased gradually until the invention of Hidden Markov Models
Speech recognition task
Getting a computer to understand spoken language
By “understand” we might mean
React appropriately
Convert the input speech into another medium, e.g. Text
LMITATIONS IN SPEECH RECOGNITION
Digitization
Converting analogue signal into digital representation
Signal processing
Separating speech from background noise
Phonetics
Variability in human speech
Phonology
Recognizing individual sound distinctions (similar phonemes)
Lexicology and syntax
Disambiguating homophones
Features of continuous speech
Syntax and pragmatics
Interpreting prosodic features
Pragmatics
Filtering of performance errors
Digitization
Analogue to digital conversion
Sampling and quantizing
Use filters to measure energy levels for various points on the frequency spectrum
Knowing the relative importance of different frequency bands (for speech) makes this process more efficient
E.g. high frequency sounds are less informative, so can be sampled using a broader bandwidth (log scale)
[attachment=17891]
INTRODUCTION
The field of Automatic Speech Recognition (ASR) is about 60 years old.
First speech recognizer was invented at BELL LABS in 1950
Development of ASR increased gradually until the invention of Hidden Markov Models
Speech recognition task
Getting a computer to understand spoken language
By “understand” we might mean
React appropriately
Convert the input speech into another medium, e.g. Text
LMITATIONS IN SPEECH RECOGNITION
Digitization
Converting analogue signal into digital representation
Signal processing
Separating speech from background noise
Phonetics
Variability in human speech
Phonology
Recognizing individual sound distinctions (similar phonemes)
Lexicology and syntax
Disambiguating homophones
Features of continuous speech
Syntax and pragmatics
Interpreting prosodic features
Pragmatics
Filtering of performance errors
Digitization
Analogue to digital conversion
Sampling and quantizing
Use filters to measure energy levels for various points on the frequency spectrum
Knowing the relative importance of different frequency bands (for speech) makes this process more efficient
E.g. high frequency sounds are less informative, so can be sampled using a broader bandwidth (log scale)