2 citations · 4 across the 5 of their papers we have counts for
6 papers
Adaptive Knowledge Distillation for Device-Directed Speech Detection
Hyung Gun Chi, Florian Pesce, Wonil Chang +5
Device-directed speech detection (DDSD) is a binary classification task that separates the user's queries to a voice assistant (VA) from background speech or side conversations. Th…
Towards a World-English Language Model for On-Device Virtual Assistants
Rricha Jalota, Lyan Verwimp, Markus Nussbaum-Thom +3
Neural Network Language Models (NNLMs) for Virtual Assistants (VAs) are generally language-, region-, and in some cases, device-dependent, which increases the effort to scale and m…
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers
Jan Silovsky, Liuhui Deng, Arturo Argueta +6
Voice technology has become ubiquitous recently. However, the accuracy, and hence experience, in different languages varies significantly, which makes the technology not equally in…
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Mingbin Xu, Congzheng Song, Ye Tian +10
Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our…
Composing Finite State Transducers on GPUs
Arturo Argueta, David Chiang
Weighted finite-state transducers (FSTs) are frequently used in language processing to handle tasks such as part-of-speech tagging and speech recognition. There has been previous w…
Decoding with Finite-State Transducers on GPUs
Arturo Argueta, David Chiang
Weighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such a…