5 papers
CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR
Muhammad Shakeel, Yosuke Fukumoto, Chikara Maeda +2
We present CALM, a joint Contextual Acoustic-Linguistic Modeling framework for multi-speaker automatic speech recognition (ASR). In personalized AI scenarios, the joint availabilit…
DYNAC: Dynamic Vocabulary based Non-Autoregressive Contextualization for Speech Recognition
Yui Sudo, Yosuke Fukumoto, Muhammad Shakeel +3
Contextual biasing (CB) improves automatic speech recognition for rare and unseen phrases. Recent studies have introduced dynamic vocabulary, which represents context phrases as ex…
Joint Beam Search Integrating CTC, Attention, and Transducer Decoders
Yui Sudo, Muhammad Shakeel, Yosuke Fukumoto +4
End-to-end automatic speech recognition (E2E-ASR) can be classified by its decoder architectures, such as connectionist temporal classification (CTC), recurrent neural network tran…
Contextualized Automatic Speech Recognition with Dynamic Vocabulary
Yui Sudo, Yosuke Fukumoto, Muhammad Shakeel +2
Deep biasing (DB) enhances the performance of end-to-end automatic speech recognition (E2E-ASR) models for rare words or contextual phrases using a bias list. However, most existin…
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search
Yui Sudo, Muhammad Shakeel, Yosuke Fukumoto +2
End-to-end (E2E) automatic speech recognition (ASR) methods exhibit remarkable performance. However, since the performance of such methods is intrinsically linked to the context pr…