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20192026
most citedA Survey on Graph Neural Networks for Knowledge Graph Completion

18 citations · 47 across the 61 of their papers we have counts for

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Showing 2023Show all

14 papers · 1 filter

cs.CL2023

Phoneme-aware Encoding for Prefix-tree-based Contextual ASR

Hayato Futami, Emiru Tsunoo, Yosuke Kashiwagi +3

In speech recognition applications, it is important to recognize context-specific rare words, such as proper nouns. Tree-constrained Pointer Generator (TCPGen) has shown promise fo…

cs.CL2023

Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data

Yifan Peng, Jinchuan Tian, Brian Yan +13

Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech dat…

cs.CL2023★ 1 cited

UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions

Siddhant Arora, Hayato Futami, Jee-weon Jung +6

Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-speci…

cs.CL2023

Semi-Autoregressive Streaming ASR With Label Context

Siddhant Arora, George Saon, Shinji Watanabe +1

Non-autoregressive (NAR) modeling has gained significant interest in speech processing since these models achieve dramatically lower inference time than autoregressive (AR) models…

eess.AS2023

Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech

Chien-yu Huang, Ke-Han Lu, Shih-Heng Wang +12

Text language models have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. However, existing studies in speech…

eess.AS2023★ 1 cited

Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation

Emiru Tsunoo, Hayato Futami, Yosuke Kashiwagi +2

Collecting audio-text pairs is expensive; however, it is much easier to access text-only data. Unless using shallow fusion, end-to-end automatic speech recognition (ASR) models req…

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…

cs.CL2023

BASS: Block-wise Adaptation for Speech Summarization

Roshan Sharma, Kenneth Zheng, Siddhant Arora +3

End-to-end speech summarization has been shown to improve performance over cascade baselines. However, such models are difficult to train on very large inputs (dozens of minutes or…

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.CL2023

A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks

Yifan Peng, Kwangyoun Kim, Felix Wu +7

Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, includi…

cs.CL2023★ 1 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.CL2023★ 1 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…

cs.CL2023

Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History

Siddhant Arora, Hayato Futami, Emiru Tsunoo +2

Most human interactions occur in the form of spoken conversations where the semantic meaning of a given utterance depends on the context. Each utterance in spoken conversation can…