Frame-by-frame language identification in short utterances using deep neural networks
By Javier Gonzalez-Dominguez, Ignacio Lopez-Moreno, Pedro J. Moreno Mengibar, Joaquin Gonzalez-Rodriguez. 2015
75 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Speech & Audio, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Daniel Povey
- Sanjeev Khudanpur
- David Snyder
- Daniel Garcia-Romero
- Gregory Sell
- Shinji Watanabe
- Alan McCree
- Yoshua Bengio
In Speech & Audio
- Sanjeev Khudanpur
- Daniel Povey
- David Snyder
- Shinji Watanabe
- Daniel Garcia-Romero
- Gregory Sell
- Alan McCree
- Fuad E. Alsaadi
In Signal Processing
- Daniel Povey
- Sanjeev Khudanpur
- David Snyder
- Daniel Garcia-Romero
- Gregory Sell
- Shinji Watanabe
- Alan McCree
- Vimal Manohar
Citations in the corpus
- TensorFlow: A system for large-scale machine learning (2016, neutral): “In recent years, machine learning has driven advances in many different fields [3, 5, 23, 24, 30, 27, 40, 45, 48, 50, 55, 68, 69, 73, 76].”
- Spoken Language Recognition using X-vectors (2018, background): “In language recognition, a popular approach is to train the neural network to classify languages at the frame-level [7, 8, 9, 10].”
- A survey of deep neural network architectures and their applications (2017, methodology): “Experiments have been carried out on short test utterance [49] from two datasets: the Google 5 million utter ances LID and the public NIST Language Recognition Evaluation dataset.”