11 citations · 24 across the 8 of their papers we have counts for
9 papers
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding
Suyoun Kim, Abhinav Arora, Duc Le +4
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 indicator…
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion
Duc Le, Mahaveer Jain, Gil Keren +9
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 for sp…
Improving RNN Transducer Based ASR with Auxiliary Tasks
Chunxi Liu, Frank Zhang, Duc Le +3
End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recogni…
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer
Suyoun Kim, Yuan Shangguan, Jay Mahadeokar +4
Recurrent Neural Network Transducer (RNN-T), like most end-to-end speech recognition model architectures, has an implicit neural network language model (NNLM) and cannot easily lev…
Cross-Attention End-to-End ASR for Two-Party Conversations
Suyoun Kim, Siddharth Dalmia, Florian Metze
We present an end-to-end speech recognition model that learns interaction between two speakers based on the turn-changing information. Unlike conventional speech recognition models…
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
Suyoun Kim, Siddharth Dalmia, Florian Metze
We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike…