5. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, identifying, for a current frame of a sequence of frames of audio data, a decoding graph state corresponding to the current frame, wherein the identifying is based at least in part on one or more frames preceding the current frame in the sequence of frames; generating a parameter of a probability density function using an artificial neural network, wherein input into the artificial neural network comprises data regarding the decoding graph state and a feature vector for each of the one or more frames preceding the current frame; generating a score using the parameter of the probability density function and a feature vector corresponding to the current frame; and generating speech recognition results using the score.
5. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, identifying, for a current frame of a sequence of frames of audio data, a decoding graph state corresponding to the current frame, wherein the identifying is based at least in part on one or more frames preceding the current frame in the sequence of frames; generating a parameter of a probability density function using an artificial neural network, wherein input into the artificial neural network comprises data regarding the decoding graph state and a feature vector for each of the one or more frames preceding the current frame; generating a score using the parameter of the probability density function and a feature vector corresponding to the current frame; and generating speech recognition results using the score. 7. The computer-implemented method of claim 5 , wherein the probability density function models a distribution of feature vectors corresponding to a given decoding graph state and a given set of preceding frames.