Search Results for author:
Found 33 papers, 8 papers with code
A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus
Over the past decade, researchers have started to explore the use of NLP to develop tools aimed at helping the public, vendors, and regulators analyze disclosures made in privacy policies.
TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages
We propose a novel Tri-Modal Translation (TMT) model that translates between arbitrary modalities spanning speech, image, and text.
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer
In this work, we aim to improve the performance and efficiency of OWSM without extra training data.
Phoneme-aware Encoding for Prefix-tree-based Contextual ASR
While the original TCPGen relies on grapheme-based encoding, we propose extending it with phoneme-aware encoding to better recognize words of unusual pronunciations.
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions
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-specific models.
Ranked #1 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Pre-training speech models on large volumes of data has achieved remarkable success.
Semi-Autoregressive Streaming ASR With Label Context
Non-autoregressive (NAR) modeling has gained significant interest in speech processing since these models achieve dramatically lower inference time than autoregressive (AR) models while also achieving good transcription accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech
To achieve comprehensive coverage of diverse speech tasks and harness instruction tuning, we invite the community to collaborate and contribute, facilitating the dynamic growth of the benchmark.
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation
Because the decoder architecture is the same as an autoregressive LM, it is simple to enhance the model by leveraging external text data with LM training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Integration of Frame- and Label-synchronous Beam Search for Streaming Encoder-decoder Speech Recognition
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 incorrect pruning.
Integrating Pretrained ASR and LM to Perform Sequence Generation for Spoken Language Understanding
There has been an increased interest in the integration of pretrained speech recognition (ASR) and language models (LM) into the SLU framework.
BASS: Block-wise Adaptation for Speech Summarization
End-to-end speech summarization has been shown to improve performance over cascade baselines.
Tensor decomposition for minimization of E2E SLU model toward on-device processing
We reduce the model size by applying tensor decomposition to the Conformer and E-Branchformer architectures used in our E2E SLU models.
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge
Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge
In the track, we adopt a pipeline approach of ASR and NLU.
Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History
Most human interactions occur in the form of spoken conversations where the semantic meaning of a given utterance depends on the context.
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks
In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape.
Streaming Joint Speech Recognition and Disfluency Detection
In this study, we propose Transformer-based encoder-decoder models that jointly solve speech recognition and disfluency detection, which work in a streaming manner.
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC).
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models
End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation.
Two-Pass Low Latency End-to-End Spoken Language Understanding
End-to-end (E2E) models are becoming increasingly popular for spoken language understanding (SLU) systems and are beginning to achieve competitive performance to pipeline-based approaches.
Creation and Analysis of an International Corpus of Privacy Laws
To examine the status and evolution of this patchwork, we introduce the Government Privacy Instructions Corpus, or GPI Corpus, of 1, 043 privacy laws, regulations, and guidelines, covering 182 jurisdictions.
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation
Although Transformers have gained success in several speech processing tasks like spoken language understanding (SLU) and speech translation (ST), achieving online processing while keeping competitive performance is still essential for real-world interaction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations
Through our evaluation, we observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding
Our splits identify performance gaps up to 10% between end-to-end systems that were within 1% of each other on the original test sets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
BERT Meets Relational DB: Contextual Representations of Relational Databases
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables.
A Survey on Graph Neural Networks for Knowledge Graph Completion
Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval.
IterefinE: Iterative KG Refinement Embeddings using Symbolic Knowledge
Knowledge Graphs (KGs) extracted from text sources are often noisy and lead to poor performance in downstream application tasks such as KG-based question answering. While much of the recent activity is focused on addressing the sparsity of KGs by using embeddings for inferring new facts, the issue of cleaning up of noise in KGs through KG refinement task is not as actively studied.
On Embeddings in Relational Databases
We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding.
Investigating Retrieval Method Selection with Axiomatic Features
We consider algorithm selection in the context of ad-hoc information retrieval.
