Abstract
This paper addresses the problem of forced alignment in news and songs in order to get the times where every word of the transcriptions begins and ends. For this purpose two methods are used. The first one is basically a forced alignment process of the audio and text based on pre-existent models. The second one is a model-free method in which new models are trained on the audio to align producing as a result the aligned text and audio. For analysis of the songs, we have considered two versions of the same song: one is an a capella song (only voice with no music) and the other, the full song (with instrumental music included). Three songs have been selected from different singers and different styles. Regarding news, we have analyzed four speakers (2 females and 2 males). Analyzing all the results, we observe that news is better aligned than songs, as expected. The two methods work similarly in both a capella songs and news, but in the case of songs that include the instrumental part, the model-free method is much better.
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Córdova Lucero, D.P., Toledano, D.T. (2012). Preliminary Results of Alignment of Text and Audio in News and Songs. In: Torre Toledano, D., et al. Advances in Speech and Language Technologies for Iberian Languages. Communications in Computer and Information Science, vol 328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35292-8_7
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DOI: https://doi.org/10.1007/978-3-642-35292-8_7
Publisher Name: Springer, Berlin, Heidelberg
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