17 citations · 43 across the 7 of their papers we have counts for
7 papers
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition
Chao-Han Huck Yang, I-Fan Chen, Andreas Stolcke +2
Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise pertu…
Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities
Pranav Dheram, Murugesan Ramakrishnan, Anirudh Raju +6
As for other forms of AI, speech recognition has recently been examined with respect to performance disparities across different user cohorts. One approach to achieve fairness in s…
Investigation of Training Label Error Impact on RNN-T
I-Fan Chen, Brian King, Jasha Droppo
In this paper, we propose an approach to quantitatively analyze impacts of different training label errors to RNN-T based ASR models. The result shows deletion errors are more harm…
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition
Maarten Van Segbroeck, Harish Mallidih, Brian King +3
Acoustic models in real-time speech recognition systems typically stack multiple unidirectional LSTM layers to process the acoustic frames over time. Performance improvements over…
Data Techniques For Online End-to-end Speech Recognition
Yang Chen, Weiran Wang, I-Fan Chen +1
Practitioners often need to build ASR systems for new use cases in a short amount of time, given limited in-domain data. While recently developed end-to-end methods largely simplif…
End-to-end Anchored Speech Recognition
Yiming Wang, Xing Fan, I-Fan Chen +3
Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device-directed speech in the presence of interfering bac…
Maximum a Posteriori Adaptation of Network Parameters in Deep Models
Zhen Huang, Sabato Marco Siniscalchi, I-Fan Chen +2
We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recogni…