Automated directory assistance system - from theory to practice

By Dong Yu, Y. Ju, Ye-Yi Wang, Geoffry Zweig, A. Acero. 2007

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence

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

1. Dong Yu
2. Li Deng
3. Jinyu Li
4. Yifan Gong
5. Frank Torsten Bernd Seide
6. Yoshua Bengio
7. S. Renals
8. Florian Metze

Citations in the corpus

• Benchmarking Deep Reinforcement Learning for Continuous Control (2016, neutral): “…(Fei-Fei et al., 2006), CI-FAR (Krizhevsky & Hinton, 2009), ImageNet (Deng et al., 2009), PASCAL VOC (Everingham et al., 2010), BSDS500 (Martin et al., 2001), SWITCHBOARD (Godfrey et al., 1992), TIMIT (Garofolo et al., 1993), Aurora (Hirsch & Pearce, 2000), and VoiceSearch (Yu et al., 2007).”
• FACTORIZED DEEP NEURAL NETWORKS FOR ADAPTIVE SPEECH RECOGNITION (2012, background): “They have achieved 16% [4][5] and 33% [6][7][8] relative recognition error reduction over strong, discriminatively trained CD-GMM-HMMs, respectively, on a voice search (VS) task [9] and the Switchboard (SWB) phone-call transcription task [10].”
• Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition (2012, methodology): “In Section IV, we analyze experimental results on a 65 vocabulary business search dataset collected from the Bing mobile voice search application (formerly known as Live Search for mobile [36], [60]) under real usage scenarios.”
• Calibration of Confidence Measures in Speech Recognition (2011, background): “I. INTRODUCTION A UTOMATIC speech recognition (ASR) technology hasbeen widely deployed in such applications as spoken dialog systems, voice mail (VM) transcription, and voice search [2], [3].”
• Large Vocabulary Speech Recognition Using Deep Tensor Neural Networks (2012, methodology): “CD-DNN-HMMs have achieved 16% [1][2] and 33% [3][4][5][6] relative recognition error reduction over strong, discriminatively trained CD-GMM-HMMs, respectively, on a voice search (VS) task [10] and the Switchboard (SWB) phone-call transcription task [11].”
• The Deep Tensor Neural Network With Applications to Large Vocabulary Speech Recognition (2013, methodology): “2227738 CD-GMM-HMMs, respectively, on a large-vocabulary voice search (VS) task [13] and the Switchboard (SWB) phone-call transcription task [14].”
• Improving wideband speech recognition using mixed-bandwidth training data in CD-DNN-HMM (2012, background): “We show that DNNs provide the flexibility of using arbitrary features.”
• KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition (2013, methodology): “Recently the context dependent deep neural network hidden Markov models (CD-DNN-HMMs) outperformed the discriminatively trained Gaussian mixture model (GMM) HMMs with 16% [1][2] and 33% [3] relative error reduction, respectively, on the Bing voice search [4] and the Switchboard (SWB) tasks.”