Rapid adaptation of speech models

A method of generating a source-adapted model for use in speech recognition, the method comprising:

• generating a collection of elements from an initial model;
• assembling speech data that corresponds to elements in the collection of elements from a set of speech data for a particular source associated with the source-adapted model;
• generating statistics from the assembled source speech data;
• modifying the statistics using an element of the initial model and a smoothing factor that accounts for an amount of source speech data available for a particular element;
• determining a transform that maps between the assembled speech data and the collection of elements of the initial model using the modified statistics;
• and
• producing elements of the source-adapted model from corresponding elements of the initial model by applying the transform to the elements of the initial model.

A method of generating a source-adapted model for use in speech recognition, the method comprising:

• generating a collection of elements from the initial model;
• assembling speech data that corresponds to elements in the collection of elements from a set of speech data for a particular source associated with the source-adapted model;
• generating statistics from the assembled source speech data;
• modifying the statistics using an element of the initial model and a smoothing factor that controls the relative importance of the element of the initial model and the assembled source speech data;
• using the modified statistics in determining a transform that maps between the assembled speech data and the collection of elements of the initial model;
• and
• producing elements of the source-adapted model from corresponding elements of the initial model by applying the transform to the elements of the initial model;
• wherein a human operator identifies classes of related elements and the collection of elements is generated using an automatic procedure that favors including elements from a common class in the collection and penalizes including elements from different classes in the collection.

A method of training a speech model for use in speech recognition, the method comprising:

• assembling sets of speech data that correspond to elements of an initial model from a set of speech data for one or more sources, the assembled speech data including multiple items;
• calculating representative values for each set of speech data;
• modifying a representative value for a set of speech data using a corresponding element of the initial model and a smoothing factor that controls the relative importance of the corresponding element of the initial model and the set of speech data;
• determining a relationship between the representative values for each set of speech data and values of the corresponding element of the initial model, where a relationship for a first set of speech data differs from a relationship for a second set of speech data;
• modifying each item of the sets of speech data using the relationship for the set to which the item belongs;
• and
• generating elements of the speech model using the modified sets of speech data.

A method of training a speech model for use in speech recognition, the method comprising:

• assembling speech data that corresponds to a first element of the speech model from a set of speech data for a first source;
• determining a transform that maps between the assembled speech data and the first element of the speech model;
• generating an inverse of the transform;
• modifying the assembled speech data using the inverse of the transform;
• and
• updating the first element of the speech model using the modified assembled speech data,
• wherein determining the transform comprises modifying the transform using the first element of the speech model and a smoothing factor that controls the relative importance of the first element of the speech model and the assembled speech data.