A method for analyzing transcriptions of a recording, comprising: storing per-character differences between a first set of characters from a first transcription of the recording and a second set of characters from a second transcription of the recording in a matrix with a fixed width, the first and second transcriptions generated by a number of automatic speech recognition engines, wherein storing the per-character differences between the first set of characters and the second set of characters in the matrix comprises: wrapping the first set of characters around the matrix by sequentially assigning each character in the first set of characters to a corresponding element (318, 320) in the matrix, such that, when characters have been assigned to all elements in a row of the matrix, additional characters in the first set of characters are assigned to elements in a subsequent row of the matrix; and storing a representation of each per-character difference between the first set of characters and the second set of characters in a corresponding position in the matrix; encoding the per-character differences in the matrix into a vector of the fixed width, the encoding comprising aggregating the per-character differences along columns of the matrix into the vector of the fixed width; outputting the vector as a representation of a pairwise error rate between the first transcription and the second transcription; and applying a machine learning model to features comprising the vector to produce a score representing an accuracy of the first transcription. storing per-character differences between a first set of characters from a first transcription of the recording and a second set of characters from a second transcription of the recording in a matrix with a fixed width, the first and second transcriptions generated by a number of automatic speech recognition engines, wherein storing the per-character differences between the first set of characters and the second set of characters in the matrix comprises: wrapping the first set of characters around the matrix by sequentially assigning each character in the first set of characters to a corresponding element (318, 320) in the matrix, such that, when characters have been assigned to all elements in a row of the matrix, additional characters in the first set of characters are assigned to elements in a subsequent row of the matrix; and storing a representation of each per-character difference between the first set of characters and the second set of characters in a corresponding position in the matrix; wrapping the first set of characters around the matrix by sequentially assigning each character in the first set of characters to a corresponding element (318, 320) in the matrix, such that, when characters have been assigned to all elements in a row of the matrix, additional characters in the first set of characters are assigned to elements in a subsequent row of the matrix; and storing a representation of each per-character difference between the first set of characters and the second set of characters in a corresponding position in the matrix; encoding the per-character differences in the matrix into a vector of the fixed width, the encoding comprising aggregating the per-character differences along columns of the matrix into the vector of the fixed width; outputting the vector as a representation of a pairwise error rate between the first transcription and the second transcription; and applying a machine learning model to features comprising the vector to produce a score representing an accuracy of the first transcription.