Methods and apparatus for generating dialog state conditioned language models

• US 7,542,901 B2
• Filed: 08/24/2006
• Issued: 06/02/2009
• Est. Priority Date: 03/27/2002
• Status: Expired due to Term

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Visweswariah, Karthik, Printz, Harry W., Monkowski, Michael Daniel, Dharanipragada, Satyanarayana
• Primary Examiner(s): Lerner; Martin
• Application Number:
  US11/509,390
• Publication Number:
  US 20060293901A1
• Time in Patent Office: 1,013 Days
• Field of Search: 704/234, 704/243, 704/244, 704/245, 704/255, 704/256, 704/257, 704/256.3
• US Class Current: 704/243
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams
• CPC Class Codes: G10L 2015/228 of application context

Abstract

Techniques are provided for generating improved language modeling. Such improved modeling is achieved by conditioning a language model on a state of a dialog for which the language model is employed. For example, the techniques of the invention may improve modeling of language for use in a speech recognizer of an automatic natural language based dialog system. Improved usability of the dialog system arises from better recognition of a user'"'"'s utterances by a speech recognizer, associated with the dialog system, using the dialog state-conditioned language models. By way of example, the state of the dialog may be quantified as: (i) the internal state of the natural language understanding part of the dialog system; or (ii) words in the prompt that the dialog system played to the user.

Claims

1. A method for use in accordance with a dialog system, the dialog system comprising a processor, the method comprising the steps of:

generating by the processor of the dialog system at least one language model, the at least one language model being conditioned on a state of dialog associated with the dialog system; and

storing the at least one language model for subsequent use in accordance with a speech recognizer associated with the dialog system;

wherein the step of generating the at least one language model conditioned on a state of dialog associated with the dialog system further comprises the processor of the dialog system performing the steps of;

dividing training data which is labeled by state into different state sets depending on the state to which the training data belongs; and

building a separate language model for each of the state sets;

wherein at least a given state corresponds to an internal state of a natural language understanding portion of the dialog system;

wherein at least a given state corresponds to a prompt that the dialog system presents to a user;

wherein at least a given separate language model is interpolated with a base model obtained from available training data for a domain of the dialog system;

wherein the method further comprises the step of clustering together two or more state sets to reduce the number of states for which a separate language model is built; and

wherein a decision to cluster two states is based on a distance measure computed between respective word distributions associated with the two states

wherein each separate language model is built using a modified Kneser-Ney smoothing technique.