A method for converting input speech to text using confidence modelling with a multiclass approach, the method comprising the steps of: accepting input speech into an automatic speech recognition system; converting the input speech into a set of features by a frontend module using a speech feature extraction method; accepting the features by a decoding module and determining the best hypotheses of the output text using an acoustic model; and applying a trained confidence model to the decoding module to obtain a probability using a multiclass classifier to predict class output text being correct, wherein the confidence model is trained to obtain the probability using the steps of: providing training examples of audio data, wherein the training examples comprise features and labels that are associated with the features, wherein the labels comprise multiclass classifiers corresponding to whether speech is converted to text correctly; generating features for training by the decoding module for each audio file in the audio data, wherein the decoding module comprises the confidence model in the automatic speech recognition system; generating hypothesized segments of speech using the generated features; evaluating the features generated by comparing each hypothesized segment of speech to a reference segment of speech; labeling comparisons of hypothesized segments to reference segments from one of a plurality of output classes 1 to n, wherein the reference segment of the output class 1 is an exact match, the reference segment of the output class n is the least likely match and the reference segments of any output classes 2 to n-1 are progressively less accurate matches, wherein the comparing comprises examining edit distance and normalized edit distance as a metric to determine a class label; and training the confidence model with the determined class labels. accepting input speech into an automatic speech recognition system; converting the input speech into a set of features by a frontend module using a speech feature extraction method; accepting the features by a decoding module and determining the best hypotheses of the output text using an acoustic model; and applying a trained confidence model to the decoding module to obtain a probability using a multiclass classifier to predict class output text being correct, wherein the confidence model is trained to obtain the probability using the steps of: providing training examples of audio data, wherein the training examples comprise features and labels that are associated with the features, wherein the labels comprise multiclass classifiers corresponding to whether speech is converted to text correctly; generating features for training by the decoding module for each audio file in the audio data, wherein the decoding module comprises the confidence model in the automatic speech recognition system; generating hypothesized segments of speech using the generated features; evaluating the features generated by comparing each hypothesized segment of speech to a reference segment of speech; labeling comparisons of hypothesized segments to reference segments from one of a plurality of output classes 1 to n, wherein the reference segment of the output class 1 is an exact match, the reference segment of the output class n is the least likely match and the reference segments of any output classes 2 to n-1 are progressively less accurate matches, wherein the comparing comprises examining edit distance and normalized edit distance as a metric to determine a class label; and training the confidence model with the determined class labels. providing training examples of audio data, wherein the training examples comprise features and labels that are associated with the features, wherein the labels comprise multiclass classifiers corresponding to whether speech is converted to text correctly; generating features for training by the decoding module for each audio file in the audio data, wherein the decoding module comprises the confidence model in the automatic speech recognition system; generating hypothesized segments of speech using the generated features; evaluating the features generated by comparing each hypothesized segment of speech to a reference segment of speech; labeling comparisons of hypothesized segments to reference segments from one of a plurality of output classes 1 to n, wherein the reference segment of the output class 1 is an exact match, the reference segment of the output class n is the least likely match and the reference segments of any output classes 2 to n-1 are progressively less accurate matches, wherein the comparing comprises examining edit distance and normalized edit distance as a metric to determine a class label; and training the confidence model with the determined class labels.