164 citations · 239 across the 25 of their papers we have counts for
26 papers
Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers
Adam Stooke, Rohit Prabhavalkar, Khe Chai Sim +1
Modern systems for automatic speech recognition, including the RNN-Transducer and Attention-based Encoder-Decoder (AED), are designed so that the encoder is not required to alter t…
TransformerFAM: Feedback attention is working memory
Dongseong Hwang, Weiran Wang, Zhuoyuan Huo +2
While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs. We propose Feedback Attention Me…
Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models
Tsendsuren Munkhdalai, Youzheng Chen, Khe Chai Sim +3
Parameter efficient adaptation methods have become a key mechanism to train large pre-trained models for downstream tasks. However, their per-task parameter overhead is considered…
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
Liam Collins, Shanshan Wu, Sewoong Oh +1
In many applications of federated learning (FL), clients desire models that are personalized using their local data, yet are also robust in the sense that they retain general globa…
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
Weiran Wang, Zelin Wu, Diamantino Caseiro +10
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scena…
Massive End-to-end Models for Short Search Queries
Weiran Wang, Rohit Prabhavalkar, Dongseong Hwang +11
In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for of…