Alexandre Trilla, PhD - Data Scientist |

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Sure, you can do that... and still get an IEEE published article

24-Dec-2012

This year has been rather prolific with respect to the attained number of research publications. The most noteworthy is the one on the IEEE Transactions on Audio, Speech and Language Processing (TASLP), which is entitled "Sentence-based Sentiment Analysis for Expressive Text-to-Speech". Its abstract is posted as follows:

"Current research to improve state of the art Text-To- Speech (TTS) synthesis studies both the processing of input text and the ability to render natural expressive speech. Focusing on the former as a front-end task in the production of synthetic speech, this article investigates the proper adaptation of a Sentiment Analysis procedure (positive/neutral/negative) that can then be used as an input feature for expressive speech synthesis. To this end, we evaluate different combinations of textual features and classifiers to determine the most appropriate adaptation procedure. The effectiveness of this scheme for Sentiment Analysis is evaluated using the Semeval 2007 dataset and a Twitter corpus, for their affective nature and their granularity at the sentence level, which is appropriate for an expressive TTS scenario. The experiments conducted validate the proposed procedure with respect to the state of the art for Sentiment Analysis."

In addition, three other publications at the SEPLN 2012 Conference (see Publications) have allowed focusing on specific aspects as subsets of a greater whole (i.e., the IEEE TASLP article). This has been hard work, indeed. And I'm proud of it. Nonetheless, I cannot help being objective about it and admit that this line of research falls into the "data porn" category (check out the "publication Markov Chain" that is being mocked there). In any case, the addressed problem is a real one and alternative sources of knowledge have been considered to solve it, so this an altogether good lesson learnt.

By the way, Merry Xmas!