5 citations · 13 across the 14 of their papers we have counts for
6 papers · 1 filter
Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder
Muhammad Shakeel, Yui Sudo, Yifan Peng +2
This paper presents a unified multi-speaker encoder (UME), a novel architecture that jointly learns representations for speaker diarization (SD), speech separation (SS), and multi-…
Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss
Muhammad Shakeel, Yui Sudo, Yifan Peng +1
Contextualized end-to-end automatic speech recognition has been an active research area, with recent efforts focusing on the implicit learning of contextual phrases based on the fi…
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…
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation
Muhammad Shakeel, Yui Sudo, Yifan Peng +1
End-to-end (E2E) automatic speech recognition (ASR) can operate in two modes: streaming and non-streaming, each with its pros and cons. Streaming ASR processes the speech frames in…
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…