Search Results for author:
Found 9 papers, 2 papers with code
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning
To this end, we extend our prior work [1], and propose a hybrid Transformer-LSTM based architecture.
Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems
In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance.
Spatial-Scale Aligned Network for Fine-Grained Recognition
Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance.
Independent language modeling architecture for end-to-end ASR
To address this problem, in this work, we propose a new architecture that separates the decoder subnet from the encoder output.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
However, learning the representation of rare words is a challenging problem causing the NLM to produce unreliable probability estimates.
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data
The lack of code-switch training data is one of the major concerns in the development of end-to-end code-switching automatic speech recognition (ASR) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition
Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances.
Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition
In this paper, we present our overall efforts to improve the performance of a code-switching speech recognition system using semi-supervised training methods from lexicon learning to acoustic modeling, on the South East Asian Mandarin-English (SEAME) data.
Fantastic 4 system for NIST 2015 Language Recognition Evaluation
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).
