4 citations · 9 across the 7 of their papers we have counts for
7 papers
Incremental Blockwise Beam Search for Simultaneous Speech Translation with Controllable Quality-Latency Tradeoff
Peter Polák, Brian Yan, Shinji Watanabe +2
Blockwise self-attentional encoder models have recently emerged as one promising end-to-end approach to simultaneous speech translation. These models employ a blockwise beam search…
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction
Jinchuan Tian, Jianwei Yu, Hangting Chen +4
Automatic speech recognition (ASR) based on transducers is widely used. In training, a transducer maximizes the summed posteriors of all paths. The path with the highest posterior…
Integration of Frame- and Label-synchronous Beam Search for Streaming Encoder-decoder Speech Recognition
Emiru Tsunoo, Hayato Futami, Yosuke Kashiwagi +2
Although frame-based models, such as CTC and transducers, have an affinity for streaming automatic speech recognition, their decoding uses no future knowledge, which could lead to…
Integrating Pretrained ASR and LM to Perform Sequence Generation for Spoken Language Understanding
Siddhant Arora, Hayato Futami, Yosuke Kashiwagi +3
There has been an increased interest in the integration of pretrained speech recognition (ASR) and language models (LM) into the SLU framework. However, prior methods often struggl…
Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute
William Chen, Xuankai Chang, Yifan Peng +3
Self-supervised learning (SSL) has led to great strides in speech processing. However, the resources needed to train these models has become prohibitively large as they continue to…
Exploration of Efficient End-to-End ASR using Discretized Input from Self-Supervised Learning
Xuankai Chang, Brian Yan, Yuya Fujita +2
Self-supervised learning (SSL) of speech has shown impressive results in speech-related tasks, particularly in automatic speech recognition (ASR). While most methods employ the out…
Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks
Jinchuan Tian, Brian Yan, Jianwei Yu +3
Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence. The Connectionist Temporal Classification (CTC) criterion is widely used in multiple seq2se…