HMM-based echo model for noise cancellation avoiding the problem of false triggers

The A method for preventing a false triggering error from an echo of any audible prompt of a plurality of audible prompts in an interactive automatic speech recognition system which uses a plurality of hidden Markov models of the system's vocabulary with each of the hidden Markov models corresponding to a phrase that is at least one word long, comprising the steps of:

• building hidden Markov models for each of said plurality of audible prompts from a respective plurality of samples of each of the audible prompt's echoes;
• receiving an input which includes signals that correspond to a caller's speech and an echo of one of the plurality of audible prompts of the interactive automatic speech response system;
• and
• using the hidden Markov models of the plurality of audible prompt echoes along with the plurality of hidden Markov models of the system's vocabulary in said automatic speech recognition system to recognize said input when an energy of the echo of the audible prompt is at least the same order of magnitude as the energy of the signals that correspond to the caller's speech instead of falsely triggering recognition of one of the plurality of hidden Markov models of the vocabulary.