17 citations · 30 across the 12 of their papers we have counts for
12 papers
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
Grant P. Strimel, Yi Xie, Brian King +3
Streaming speech recognition architectures are employed for low-latency, real-time applications. Such architectures are often characterized by their causality. Causal architectures…
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition
Feng-Ju Chang, Anastasios Alexandridis, Rupak Vignesh Swaminathan +6
To achieve robust far-field automatic speech recognition (ASR), existing techniques typically employ an acoustic front end (AFE) cascaded with a neural transducer (NT) ASR model. T…
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…
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation
Viet Anh Trinh, Pegah Ghahremani, Brian King +3
We present an approach to reduce the performance disparity between geographic regions without degrading performance on the overall user population for ASR. A popular approach is to…
Compute Cost Amortized Transformer for Streaming ASR
Yi Xie, Jonathan Macoskey, Martin Radfar +5
We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization. Ou…
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…