4. A computer-readable hardware medium storing computer-executable program instructions that when executed cause a computing system to: compute a frame posterior for each word in an utterance from a corpus comprising audio data and a corresponding transcription that contains transcription errors, wherein the instructions to compute the frame posterior include instructions that when executed cause the computing system to: decode the audio data using an existing acoustic model to generate a lattice, merging the decoded lattice with the transcription, labeling each word in the merged lattice as one of correct or incorrect by examining a percentage to which the word is overlapped in duration with the transcription, computing a posterior probability for each word in the merged lattice, and computing the frame posterior q(t) of time t by summing the posterior probabilities of all the correct words passing time t for a time interval; train an acoustic model with confidence-based maximum likelihood estimation (MLE) training using the frame posterior by estimating acoustic model parameters using the transcription, the audio data and the frame posterior; estimate the acoustic model parameters with confidence-based discriminative training using the frame posterior; evaluate the accuracy of the acoustic model built from the corpus including the corresponding transcription that contains transcription errors compared to the accuracy of an acoustic model built from a similar amount of training data having no transcription errors; and generate a finalized acoustic model.
4. A computer-readable hardware medium storing computer-executable program instructions that when executed cause a computing system to: compute a frame posterior for each word in an utterance from a corpus comprising audio data and a corresponding transcription that contains transcription errors, wherein the instructions to compute the frame posterior include instructions that when executed cause the computing system to: decode the audio data using an existing acoustic model to generate a lattice, merging the decoded lattice with the transcription, labeling each word in the merged lattice as one of correct or incorrect by examining a percentage to which the word is overlapped in duration with the transcription, computing a posterior probability for each word in the merged lattice, and computing the frame posterior q(t) of time t by summing the posterior probabilities of all the correct words passing time t for a time interval; train an acoustic model with confidence-based maximum likelihood estimation (MLE) training using the frame posterior by estimating acoustic model parameters using the transcription, the audio data and the frame posterior; estimate the acoustic model parameters with confidence-based discriminative training using the frame posterior; evaluate the accuracy of the acoustic model built from the corpus including the corresponding transcription that contains transcription errors compared to the accuracy of an acoustic model built from a similar amount of training data having no transcription errors; and generate a finalized acoustic model. 5. The computer-readable hardware medium of claim 4 , wherein the instructions to estimate model parameters include instructions that when executed cause the computing system to: calculate the update formulas for mean (μ jk ) and variance (σ jk 2 ) for a jth state and a kth mixture model as: μ jk = ∑ t = 1 T ⁢ ζ _ jk ⁡ ( t ) ⁢ O ⁡ ( t ) ∑ t = 1 T ⁢ ζ _ jk ⁡ ( t ) σ jk 2 = ∑ t = 1 T ⁢ ζ _ jk ⁡ ( t ) ⁢ ( O ⁡ ( t ) - μ jk ′ ) ⁢ ( O ⁡ ( t ) - μ jk ′ ) t ∑ t = 1 T ⁢ ζ _ jk ⁡ ( t ) wherein ζ jk (t) is a posterior adjusted according to the frame posterior.