1. A method comprising: receiving automatic speech recognition output of a plurality of speech interactions with a user; receiving an automatic speech recognition transcription error model characterizing how automatic speech recognition transcription errors are made; generating, via a processor, guesses of a true transcription and a user model via an expectation maximization algorithm, wherein the expectation maximization algorithm is based on the error model and the automatic speech recognition output; and generating a personalized user model associated with a user voiceprint of the user based on the guesses and the user model.
1. A method comprising: receiving automatic speech recognition output of a plurality of speech interactions with a user; receiving an automatic speech recognition transcription error model characterizing how automatic speech recognition transcription errors are made; generating, via a processor, guesses of a true transcription and a user model via an expectation maximization algorithm, wherein the expectation maximization algorithm is based on the error model and the automatic speech recognition output; and generating a personalized user model associated with a user voiceprint of the user based on the guesses and the user model. 3. The method of claim 1 , wherein the expectation maximization algorithm estimates conditional probabilities of hidden variables.