The MIT Mobile Device Speaker Verification Corpus: Data Collection and Preliminary Experiments
By Ram H. Woo, Alex Seungryong Park, Timothy J. Hazen. 2006
89 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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
- Andrew Zisserman
- Joon Son Chung
- Arsha Nagrani
- Vishal M. Patel
- Ramalingam Chellappa
- Andrea Vedaldi
- Kristen Grauman
- Yoshua Bengio
In Signal Processing
- Andrew Zisserman
- Arsha Nagrani
- Joon Son Chung
- Vishal M. Patel
- Ramalingam Chellappa
- Triantafyllos Afouras
- Andrew Owens
- Andrea Vedaldi
Citations in the corpus
- VoxCeleb: A Large-Scale Speaker Identification Dataset (2017, background): “Many existing datasets are obtained under controlled conditions, for example: forensic data intercepted by police of-ficials [16], data from telephone calls [17], speech recorded live in high quality environments such as acoustic laboratories [18, 19], or speech recorded from mobile devices [20, 21].”
- Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, background): “Furthermore, [55] studied the feasibility of voice biometrics on mobile devices.”
- Front-End Factor Analysis For Speaker Verification (2018, methodology): “The exception is most likely caused by an undesired high frequency tone present in the utterances recorded in the quiet office with the internal microphone ([4] presents an example spectrogram corresponding to such an utterance affected by an undesired high frequency tone).”