Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data

Abstract

This paper describes the method that was used to produce additional acoustic model training data for the less-resourced languages of Lithuanian and Latvian. The method uses existing baseline speech recognition systems for Latvian and Lithuanian to align audio data from the Web with imprecise non-normalised transcripts. From 690 hours of Web data (300h for Latvian, 390h for Lithuanian), we have created additional 378 hours of training data (186h for Latvian and 192 for Lithuanian). Combining this additional data with baseline training data allowed to significantly improve word error rate for Lithuanian from 40% to 23%. Word error rate for the Latvian system was improved from 19% to 17%.

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