Search Results for author:
Found 16 papers, 2 papers with code
Contextualization of ASR with LLM using phonetic retrieval-based augmentation
Large language models (LLMs) have shown superb capability of modeling multimodal signals including audio and text, allowing the model to generate spoken or textual response given a speech input.
Enhancing CTC-based speech recognition with diverse modeling units
In recent years, the evolution of end-to-end (E2E) automatic speech recognition (ASR) models has been remarkable, largely due to advances in deep learning architectures like transformer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices
With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge.
Acoustic Model Fusion for End-to-end Speech Recognition
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.
A Multi-task Learning Approach for Named Entity Recognition using Local Detection
As a result, we observed competitive performance in nearly all of the tasks.
Effective Context and Fragment Feature Usage for Named Entity Recognition
In this paper, we explore a new approach to named entity recognition (NER) with the goal of learning from context and fragment features more effectively, contributing to the improvement of overall recognition performance.
A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases
Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP.
Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation
In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem.
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models
In this paper, we propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) (Zhang et al., 2015b) in neural languages modelling, called dual-FOFE.
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding
In this paper, we propose to learn word embeddings based on the recent fixed-size ordinally forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence into a fixed-size representation.
A Local Detection Approach for Named Entity Recognition and Mention Detection
In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing.
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing.
A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation.
