How to leverage dynamic contextual information in end-to-end speech recognition has
remained an active research area. Previous solutions to this problem were either designed …

Virtual assistants are becoming increasingly important speech-driven Information Retrieval
platforms that assist users with various tasks. We discuss open problems and challenges …

The efficacy of external language model (LM) integration with existing end-to-end (E2E)
automatic speech recognition (ASR) systems can be improved significantly using the …

State-of-the-art neural network language models (NNLMs) represented by long short term
memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly …

Contextual knowledge is important for real-world automatic speech recognition (ASR)
applications. In this paper, a novel tree-constrained pointer generator (TCPGen) component …

Contextual information plays a crucial role in speech recognition technologies and
incorporating it into the end-to-end speech recognition models has drawn immense interest …

Word Error Rate (WER) has been the predominant metric used to evaluate the performance
of automatic speech recognition (ASR) systems. However, WER is sometimes not a good …

By incorporating additional contextual information, deep biasing methods have emerged as
a promising solution for speech recognition of personalized words. However, for real-world …

As speech-enabled devices such as smartphones and smart speakers become increasingly
ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems …

The research problem addressed in this study is how to effectively combine multimodal data
from imperfect text transcripts and raw audio in a deep framework for automatic speech …