Pocketsphinx: A Free, Real-Time Continuous Speech Recognition System for Hand-Held Devices
By David Huggins-Daines, Mohit Kumar, Arthur Chan, Alan W. Black, M. Ravishankar, Alexander I. Rudnicky. 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006
480 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/5133cdde590b549d16b58bb0376ab11a518afb58
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
- Louis-philippe Morency
- Antoine Raux
- Sungho Shin
- Percy Liang
- Jason Williams
- Yoonho Boo
- Wonyong Sung
- Alan W. Black
In Signal Processing
- Louis-philippe Morency
- Sungho Shin
- Yoonho Boo
- Wonyong Sung
- Iksoo Choi
- Alan W. Black
- Alexander I. Rudnicky
- Jinhwan Park
Citations in the corpus
- The Dialog State Tracking Challenge (2013, methodology): “Group A systems produced only 1best lists, so for Group A systems, recognition was re-run with the Pocketsphinx speech recognizer (Huggins-Daines et al., 2006) with N-best output enabled, and the results were included in the log files.”
- Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices (2018, background): “Therefore, there is a huge demand for on-device ASR implementation not only for smartphones but also for many internet of things (IoT) devices [7].”
- SimSensei kiosk: a virtual human interviewer for healthcare decision support (2014, methodology): “In the experiments reported in this paper, voice activity detection and speech recognition were performed using PocketSphinx [15].”
- Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases (2013, methodology): “We obtained the ASR output for the Navagati corpus by running the test utterances through PocketSphinx (Huggins-Daines et al., 2006).”