• DOI:10.21437/interspeech.2019-2998
Corpus ID: 195766833

Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voice

@article{Mitra2019LeveragingAC,
  title={Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voice},
  author={V. Mitra and Sue Booker and E. Marchi and David Scott Farrar and Ute Dorothea Peitz and Bridget J. Cheng and Ermine A. Teves and A. Mehta and Devang Naik},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.00112}
}

V. Mitra, Sue Booker, +6 authors Devang Naik

Published 2019

Computer Science, Engineering

ArXiv

Millions of people reach out to digital assistants such as Siri every day, asking for information, making phone calls, seeking assistance, and much more. The expectation is that such assistants should understand the intent of the users query. Detecting the intent of a query from a short, isolated utterance is a difficult task. Intent cannot always be obtained from speech-recognized transcriptions. A transcription driven approach can interpret what has been said but fails to acknowledge how it… CONTINUE READING

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