Method and system for generating squeezed acoustic models for specialized speech recognizer

A computer-based method of automatically generating from a first speech recognizer, a second speech recognizer, wherein the first speech recognizer includes a set of states and a set of probability density functions assembling output probabilities for an observation of a speech frame in the states, the method comprising the steps of:

• generating, from the set of states of the first speech recognizer, a set of states of the second speech recognizer by selecting a subset of states of the first speech recognizer being distinctive of a particular application;
• and
• generating, from the set of probability density functions of the first speech recognizer, a set of probability density functions of the second speech recognizer by selecting a subset of probability density functions of the first speech recognizer being distinctive of the particular application, such that the second speech recognizer is at least one of tailored to the particular application and requires reduced resources compared to the first speech recognizer;
• wherein selecting the subset of probability density functions comprises the steps of:
• identifying a more reliably classifiable state by associating a multitude of speech frames of application-specific training data with correct states of the first speech recognizer;
• determining, for a certain state, a reliability value representing a reliability measurement for the certain state;
• and
• identifying the certain state as a more reliably classifiable state when the reliability value is above a threshold.

Apparatus for automatically generating, from a first speech recognizer, a second speech recognizer, wherein the first speech recognizer includes a set of states and a set of probability density functions assembling output probabilities for an observation of a speech frame in the states, the apparatus comprising:

• at least one processor operative to:
• (i) generate, from the set of states of the first speech recognizer, a set of states of the second speech recognizer by selecting a subset of states of the first speech recognizer being distinctive of a particular application;
• and (ii) generate, from the set of probability density functions of the first speech recognizer, a set of probability density functions of the second speech recognizer by selecting a subset of probability density functions of the first speech recognizer being distinctive of the particular application, such that the second speech recognizer is at least one of tailored to the particular application and requires reduced resources compared to the first speech recognizer;
• wherein the operation of selecting the subset of probability density functions comprises:
• (i) identifying a more reliably classifiable state by associating a multitude of speech frames of application-specific training data with correct states of the first speech recognizer;
• (ii) determining, for a certain state, a reliability value representing a reliability measurement for the certain state;
• and (iii) identifying the certain state as a more reliably classifiable state when the reliability value is above a threshold.

An article of manufacture for automatically generating, from a first speech recognizer, a second speech recognizer, wherein the first speech recognizer includes a set of states and a set of probability density functions assembling output probabilities for an observation of a speech frame in the states, comprising a machine readable medium containing one or more programs which when executed implement the steps of:

• generating, from the set of states of the first speech recognizer, a set of states of the second speech recognizer by selecting a subset of states of the first speech recognizer being distinctive of a particular application;
• and
• generating, from the set of probability density functions of the first speech recognizer, a set of probability density functions of the second speech recognizer by selecting a subset of probability density functions of the first speech recognizer being distinctive of the particular application, such that the second speech recognizer is at least one of tailored to the particular application and requires reduced resources compared to the first speech recognizer;
• wherein selecting the subset of probability density functions comprises the steps of:
• identifying a more reliably classifiable state by associating a multitude of speech frames of application-specific training data with correct states of the first speech recognizer;
• determining, for a certain state, a reliability value representing a reliability measurement for the certain state;
• and
• identifying the certain state as a more reliably classifiable state when the reliability value is above a threshold.