127 citations · 255 across the 15 of their papers we have counts for
6 papers · 1 filter
LAST: Scalable Lattice-Based Speech Modelling in JAX
Ke Wu, Ehsan Variani, Tom Bagby +1
We introduce LAST, a LAttice-based Speech Transducer library in JAX. With an emphasis on flexibility, ease-of-use, and scalability, LAST implements differentiable weighted finite s…
Alignment Entropy Regularization
Ehsan Variani, Ke Wu, David Rybach +2
Existing training criteria in automatic speech recognition(ASR) permit the model to freely explore more than one time alignments between the feature and label sequences. In this pa…
Federated Learning of N-gram Language Models
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews +4
We propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to trai…
Approximating probabilistic models as weighted finite automata
Ananda Theertha Suresh, Brian Roark, Michael Riley +1
Weighted finite automata (WFA) are often used to represent probabilistic models, such as -gram language models, since they are efficient for recognition tasks in time and space.…
Mobile Keyboard Input Decoding with Finite-State Transducers
Tom Ouyang, David Rybach, Françoise Beaufays +1
We propose a finite-state transducer (FST) representation for the models used to decode keyboard inputs on mobile devices. Drawing from learnings from the field of speech recogniti…
Weighted Automata in Text and Speech Processing
Mehryar Mohri, Fernando Pereira, Michael Riley
Finite-state automata are a very effective tool in natural language processing. However, in a variety of applications and especially in speech precessing, it is necessary to consid…