System and method for generating models for use in automatic speech recognition

A system comprising:

• a processor;
• and
• a computer-readable storage medium having instructions stored which, when executed by the processor, result in the processor performing operations comprising:
• receiving, from a separate entity, a request to generate a model, input data and a seed model;
• receiving a cost function associated with generation of the model, the cost function indicating an accuracy and one of speed and memory usage, wherein the cost function is formulated as:
• Accscore(ASR(Xi))=f(Xi)→word accuracy and speed
• where the following definitions apply:
• fmin(Xi)=−1* (word accuracy−β*speed),
• speed=(CPU time)/(audio time),
• β=a weighting factor to speed that provides a tradeoff between accuracy and speed
• Xi=a set of parameters that affect accuracy and speed, such as beam width, LM scale, MAP multiplier, maximum active arcs, duration scale;
• processing the input data based on the seed model and based on parameters that modify one of the accuracy and the one of speed and memory of the cost function, to yield an updated model;
• and
• outputting the updated model.

A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:

• receiving, from a separate entity, of a request to generate a model, input data and a seed model;
• receiving a cost function associated with generation of the model, the cost function indicating accuracy and one of speed and memory usage, wherein the cost function is formulated as:
• Accscore(ASR(Xi))=f(Xi)→word accuracy and speed
• where the following definitions apply:
• fmin(Xi)=−1* (word accuracy−β*speed),
• speed=(CPU time)/(audio time),
• β=a weighting factor to speed that provides a tradeoff between accuracy and speed,
• Xi=a set of parameters that affect accuracy and speed, such as beam width, LM scale, MAP multiplier, maximum active arcs, duration scale;
• processing the input data based on the seed model and based on parameters that modify the accuracy and the one of speed and memory usage of the cost function, to yield an updated model;
• and
• outputting the updated model.