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  # The Serbian Parliamentary Spoken Dataset ParlaSpeech-RS 1.0
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- http://hdl.handle.net/11356/1834
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- The ParlaSpeech-RS dataset is built from the transcripts of parliamentary proceedings available in the Serbian part of the ParlaMint corpus, and the parliamentary recordings available from the Serbian Parliament's YouTube channel.
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- The corpus consists of audio segments that correspond to specific sentences in the transcripts. The transcript contains word-level alignments to the recordings, each instance consisting of character and millisecond start and end offsets, allowing for simple further segmentation of long sentences into shorter segments for ASR and other memory-sensitive applications. Sequences longer than 30 seconds have already been removed from this dataset, which should allow for a simple usage on most modern GPUs.
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  Each segment has an identifier reference to the ParlaMint 4.0 corpus (http://hdl.handle.net/11356/1859) via the utterance ID and character offsets.
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  # The Serbian Parliamentary Spoken Dataset ParlaSpeech-RS 1.0
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+ The master dataset can be found at http://hdl.handle.net/11356/1834.
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+ The ParlaSpeech-RS dataset is built from the transcripts of parliamentary proceedings available in the Serbian part of the ParlaMint corpus (http://hdl.handle.net/11356/1859), and the parliamentary recordings available from the Serbian Parliament's YouTube channel (https://www.youtube.com/user/ParlamentSrbija).
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+ The dataset consists of audio segments that correspond to specific sentences in the transcripts. The transcript contains word-level alignments to the recordings, each instance consisting of character and millisecond start and end offsets, allowing for simple further segmentation of long sentences into shorter segments for ASR and other memory-sensitive applications. Sequences longer than 30 seconds have already been removed from this dataset, which should allow for a simple usage on most modern GPUs.
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  Each segment has an identifier reference to the ParlaMint 4.0 corpus (http://hdl.handle.net/11356/1859) via the utterance ID and character offsets.
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