Spoken dialog system using a best-fit language model and best-fit grammar

A spoken dialog system using best-fit grammar, comprising:

• a dialog manager, coupled to a grammar selector, that provides to the grammar selector a current dialog state;
• the grammar selector, coupled to a plurality of dialog-state dependent sub-grammars, that selects one of the plurality of dialog-state dependent sub-grammars;
• the plurality of dialog-state dependent sub-grammars, that contain a plurality of speech patterns;
• a speech recognition module, coupled to the dialog manager and the grammar selector, that receives input speech;
• a general-purpose grammar, coupled to the grammar selector, that contains patterns of general user responses;
• and
• a plurality of dialog strategies based on the selected grammar system, coupled to the dialog manager, to enhance dialog performance.

A spoken dialog system implementing a best-fit language model, comprising a computer readable medium and a computer readable program code stored on the computer readable medium having instructions to:

• receive a current dialog state from a dialog manager;
• select a dialog-state dependent language model from a plurality of dialog-state dependent language models based on the current dialog state;
• generate a first hypothesis result for input speech with a first likelihood score;
• generate a second hypothesis result for input speech with a second likelihood score;
• select a best-fit language model from the higher value of the first likelihood score and the second likelihood score;
• and implement dialog strategies, based on the best-fit language model, to improve dialog performance.

A spoken dialog system implementing best-fit grammar, comprising a computer readable medium and a computer readable program code stored on the computer readable medium having instructions to:

• receive a current dialog state from a dialog manager;
• select one of a plurality of dialog-state dependent sub-grammars based on the current dialog state;
• select a general-purpose grammar, if the chosen dialog-state dependent sub-grammar fails to provide a matching pattern of input speech;
• and
• implement dialog strategies, based on one of the dialog-state dependent sub-grammar and the general-purpose grammar, to improve dialog performance.

A method of implementing a best-fit language model in a spoken dialog system, comprising:

• receiving a current dialog state from a dialog manager;
• choosing a dialog-state dependent language model from a plurality of dialog-state dependent language models based on the current dialog state;
• calculating a first hypothesis result for input speech with a first likelihood score;
• calculating a second hypothesis result for input speech with a second likelihood score;
• choosing a best-fit language model from the higher value of the first likelihood score and the second likelihood score;
• and
• deploying dialog strategies, based on the best-fit language model, to improve dialog performance.

A method of implementing a best-fit grammar model in a spoken dialog system, comprising:

• receiving a current dialog state from a dialog manager;
• choosing one of a plurality of dialog-state dependent sub-grammars based on the current dialog state;
• choosing a general-purpose grammar, if the chosen dialog-state dependent sub-grammar fails to provide a matching pattern of input speech;
• and
• employing dialog strategies, based on one of the dialog-state dependent sub-grammar and the general-purpose grammar, to improve dialog performance.

A spoken dialog system using a best-fit language model and best-fit grammar, comprising:

• a dialog manager, coupled to a language model selector and a grammar selector, that provides to the language model selector and to the grammar selector a current dialog state;
• the language model selector, coupled to a plurality of dialog-state dependent language models, that selects one of the plurality of dialog-state dependent language models;
• the grammar selector, coupled to a plurality of dialog-state dependent sub-grammars, that contain a plurality of speech patterns;
• the plurality of dialog-state dependent language models that are interpolated from a general-task language model;
• the plurality of dialog-state dependent sub-grammars that contain a plurality of speech patterns;
• a large vocabulary continuous speech recognizer, coupled to the dialog manager and the language model selector and a language understanding module, that receives input speech and generates a first hypothesis result for the input speech with a likelihood score, based on the selected dialog-state dependent language model;
• the language understanding module, coupled to the dialog manager and to the grammar selector and to large vocabulary continuous speech recognizer, that extracts critical information from a word sequence generated by the large vocabulary continuous speech recognizer;
• the general-task language model, coupled to the large vocabulary continuous speech recognizer, that enables the large vocabulary continuous speech recognizer to generate a second hypothesis with a second likelihood score;
• a general-purpose grammar, coupled to the grammar selector, that contains patterns of general user responses;
• and
• a plurality of dialog strategies based on the language model system and a plurality of dialog strategies based on the selected grammar system, coupled to the dialog manager.

A spoken dialog system implementing a best-fit language model and best-fit grammar, comprising a computer readable medium and a computer readable program code stored on the computer readable medium having instructions to:

• receive a current dialog state from a dialog manager;
• select a dialog-state dependent language model from a plurality of dialog-state dependent language models based on the current dialog state;
• select a dialog-state dependent sub-grammar from a plurality of dialog-state dependent sub-grammars based on the current dialog state;
• select a general-purpose grammar, if the chosen dialog-state dependent sub-grammar fails to provide a matching pattern of input speech;
• generate a first hypothesis result for input speech with a first likelihood score;
• generate a second hypothesis result for input speech with a second likelihood score;
• select a best-fit language model from the higher value of the first likelihood score and the second likelihood score;
• and
• implement dialog strategies, based on the best-fit language model and based on one of the dialog-state dependent sub-grammar and the general-purpose grammar.