This paper presents results for large vocabulary continuous speech recognition (LVCSR) in Swedish. We trained acoustic models on the public domain NST Swedish corpus and made them freely available to the community. The training procedure corresponds to the reference recogniser (RefRec) developed for the SpeechDat databases during the COST249 action. We describe the modifications we made to the procedure in order to train on the NST database, and the language models we created based on the N-gram data available at the Norwegian Language Council. Our tests include medium vocabulary isolated word recognition and LVCSR. Because no previous results are available for LVCSR in Swedish, we use as baseline the performance of the SpeechDat models on the same tasks. We also compare our best results to the ones obtained in similar conditions on resource rich languages such as American English. We tested the acoustic models with HTK and Julius and plan to make them available in CMU Sphinx format as well in the near future. We believe that the free availability of these resources will boost research in speech and language technology in Swedish, even in research groups that do not have resources to develop ASR systems.

This paper presents a plugin that adds automatic speech recognition (ASR) functionality to the WaveSurfer sound manipulation and visualisation program. The plugin allows the user to run continuous speech recognition on spoken utterances, or to align an already available orthographic transcription to the spoken material. The plugin is distributed as free software and is based on free resources, namely the Julius speech recognition engine and a number of freely available ASR resources for different languages. Among these are the acoustic and language models we have created for Swedish using the NST database.