hidden markov model thresholding
algorithm, artificial-intelligence, hidden-markov-models, java, speech-recognition
Solution
To reject other words you need a filler model.
This is a statistical hypothesis test. You have two hypothesis (word is known and word is unknown). To make a decision you need to estimate a probability of each hypothesis.
Filler model is trained from the speech you have, just in a different way, for example it might be a single gaussian for any speech sound. You compare score from generic filler model and score from the word HMM and make a decision. For more in-depth information and advanced algorithms you can check any paper on keyword spotting. This thesis have a good review:
ACOUSTIC KEYWORD SPOTTING IN SPEECH WITH APPLICATIONS TO DATA MINING A. J. Kishan Thambiratnam
http://eprints.qut.edu.au/37254/1/Albert_Thambiratnam_Thesis.pdf
Problem
I have developed a proof of concept system for sound recognition using mfcc and hidden markov models. It gives promising results when I test the system on known sounds. Although the system, when an unknown sound is inputted returns the result with the closest match and the score is not that distinct to devise it is an unknown sound e.g.: I have trained 3 hidden markov models one for speech, one for water coming out of water tap and one for knocking on the desk. Then I test them on unseen data and get following results: ``` input: speech HMM\knocking: -1213.8911146444477 HMM\speech: -617.8735676792728 HMM\watertap: -1504.4735097322673 So highest score speech which is correct input: watertap HMM\knocking: -3715.7246152783955 HMM\speech: -4302.67960438553 HMM\watertap: -1965.6149147201534 So highest score watertap which is correct input: knocking HMM\filler -806.7248912250212 HMM\knocking: -756.4428782636676 HMM\speech: -1201.686687761133 HMM\watertap: -3025.181144273698 So highest score knocking which is correct input: unknown HMM\knocking: -4369.1702184688975 HMM\speech: -5090.37122832872 HMM\watertap: -7717.501505674925 ``` Here the input is an unknown sound but it still returns the closest match as there is no system for thresholding/garbage filtering. I know that in keyword spotting an OOV (out of vocabulary) sound can be filtered out using a garbage or filler model but it says it is trained using a finite set of unknown words where this can't be applied to my system as I don't know all the sounds that the system may record. How is a similar problem solved in speech recognition system? And how can I solve my problem to avoid false positives?