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IEEE/ACM Transactions on Audio, Speech and Language Processing
  1-4 of 4 reviews Date Reviewed 
  Curriculum learning for speech emotion recognition from crowdsourced labels
Lotfian R., Busso C.  IEEE/ACM Transactions on Audio, Speech and Language Processing 27(4): 815-826, 2019. Type: Article

In computer applications such as synergistic games, gratifying robots, and speech recognition systems, the ability to identify emotions is invaluable. But how should effective algorithms and systems be designed for discerning emotions from diverse...

Aug 20 2021
  Unsupervised feature learning based on deep models for environmental audio tagging
Xu Y., Huang Q., Wang W., Foster P., Sigtia S., Jackson P., Plumbley M.  IEEE/ACM Transactions on Audio, Speech and Language Processing 25(6): 1230-1241, 2017. Type: Article

The paper promises tagging--this is just the labeling of audio, which is referred to as “tagging” in the context of audio--of environmental audio using a deep network of learned features. A multi-label approach learned using ...

Jun 15 2018
  Real-time audio-to-score alignment of music performances containing errors and arbitrary repeats and skips
Nakamura T., Nakamura E., Sagayama S.  IEEE/ACM Transactions on Audio, Speech and Language Processing 24(2): 329-339, 2016. Type: Article

The primary contribution of this paper is the development and presentation of two hidden Markov models (HMM) that can follow a monophonic audio performance in real time and recognize arbitrary repeats/skips and other errors in a performance....

Aug 12 2016
  Sequential summarization: a full view of Twitter trending topics
Gao D., Mladenovic M., Li W., Cai X., Zhang R., Ouyang Y.  IEEE/ACM Transactions on Audio, Speech and Language Processing 22(2): 293-302, 2014. Type: Article

This paper describes an approach to summarize trending topics on Twitter. The tweets for a given topic are linearly segmented into more granular subtopics that define a single action or event....

Jul 11 2014
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