most citedThe Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2023

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…

cs.CL2023

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…

eess.AS2023

Tensor decomposition for minimization of E2E SLU model toward on-device processing

Yosuke Kashiwagi, Siddhant Arora, Hayato Futami +6

Spoken Language Understanding (SLU) is a critical speech recognition application and is often deployed on edge devices. Consequently, on-device processing plays a significant role…

cs.CL20231 cited

The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge

Hayato Futami, Jessica Huynh, Siddhant Arora +6

This paper describes our system for the low-resource domain adaptation track (Track 3) in Spoken Language Understanding Grand Challenge, which is a part of ICASSP Signal Processing…

cs.CL20231 cited

A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge

Siddhant Arora, Hayato Futami, Shih-Lun Wu +6

Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken sem…