US 6,151,574 A · Technique for adaptation of hidden markov models for speech recognition

Abstract

A speech recognition system learns characteristics of speech by a user during a learning phase to improve its performance. Adaptation data derived from the user's speech and its recognized result is collected during the learning phase. Parameters characterizing hidden Markov Models (HMMs) used in the system for speech recognition are modified based on the adaptation data. To that end, a hierarchical structure is defined in an HMM parameter space. This structure may assume the form of a tree structure having multiple layers, each of which includes one or more nodes. Each node on each layer is connected to at least one node on another layer. The nodes on the lowest layer of the tree structure are referred to as "leaf nodes." Each node in the tree structure represents a subset of the HMM parameters, and is associated with a probability measure which is derived from the adaptation data. In particular, each leaf node represents a different one of the HMM parameters, which is derivable from the probability measure associated with the leaf node. This probability measure is a function of the probability measures which are associated with the nodes connected to the leaf node, and which represent "hierarchical priors" to such a probability measure.

Claims38

Dependent claims

Apparatus for recognizing selected speech based on acoustic models, the apparatus comprising:

a processor responsive to selected data representing a sample of the selected speech for modifying the acoustic models;
and
a mechanism for defining a hierarchical structure which includes a plurality of levels, each level having one or more nodes thereon, each level in the structure arranged higher than every other level having a smaller number of nodes thereon than the other level, each node being associated with a probability measure which is determined based on at least the selected data, each node on each level having two or more nodes thereon being connected to a node on a second level higher than the level having two or more nodes thereon, the probability measure associated with each node on each level having two or more nodes thereon being a function of at least the probability measure associated with the node connected thereto on the second level, the acoustic models being modified based on at least the probability measure associated with each node on a selected level.

Dependencies are inferred from the claim text.

Technique for adaptation of hidden markov models for speech recognition

Life of this patent

Technique for adaptation of hidden markov models for speech recognition

Life of this patent

Abstract

Claims38