Abstract: A computer-implemented technique identifies terms in an original reference transcription and original ASR output results that are considered valid variants of each other, even though these terms have different textual forms. Based on this finding, the technique produces a normalized reference transcription and normalized ASR output results in which valid variants are assigned the same textual form. In some implementations, the technique uses the normalized text to develop a model for an ASR system. For example, the technique may generate a word error rate (WER) measure by comparing the normalized reference transcription with the normalized ASR output results, and use the WER measure as guidance in developing the model. Some aspects of the technique involve identifying occasions in which a term can be properly split into component parts. Other aspects can identify other ways in which two terms may vary in spelling, but nonetheless remain valid variants.

Abstract: A training-stage technique trains a language model for use in an ASR system. The technique includes: obtaining a training corpus that includes a sequence of terms; determining that an original term in the training corpus is not present in a dictionary resource; segmenting the original term into two or more sub-terms using a segmentation resource; determining that the segmentation of the original term into the two or more sub-terms is a valid segmentation, based on two or more validity tests; and training the language model based on the terms that have been identified. A computer-implemented inference-stage technique applies the language model to produce ASR output results. The inference-stage technique merges a sub-term with a preceding term if these two terms are separated by no more than a prescribed interval of time.

Abstract: A training-stage technique trains a language model for use in an ASR system. The technique includes: obtaining a training corpus that includes a sequence of terms; determining that an original term in the training corpus is not present in a dictionary resource; segmenting the original term into two or more sub-terms using a segmentation resource; determining that the segmentation of the original term into the two or more sub-terms is a valid segmentation, based on two or more validity tests; and training the language model based on the terms that have been identified. A computer-implemented inference-stage technique applies the language model to produce ASR output results. The inference-stage technique merges a sub-term with a preceding term if these two terms are separated by no more than a prescribed interval of time.

Abstract: A computer-implemented technique identifies terms in an original reference transcription and original ASR output results that are considered valid variants of each other, even though these terms have different textual forms. Based on this finding, the technique produces a normalized reference transcription and normalized ASR output results in which valid variants are assigned the same textual form. In some implementations, the technique uses the normalized text to develop a model for an ASR system. For example, the technique may generate a word error rate (WER) measure by comparing the normalized reference transcription with the normalized ASR output results, and use the WER measure as guidance in developing the model. Some aspects of the technique involve identifying occasions in which a term can be properly split into component parts. Other aspects can identify other ways in which two terms may vary in spelling, but nonetheless remain valid variants.