Search Results for author:
Found 8 papers, 0 papers with code
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)
+4
Neural Language Model Pruning for Automatic Speech Recognition
We study model pruning methods applied to Transformer-based neural network language models for automatic speech recognition.
Space-Efficient Representation of Entity-centric Query Language Models
Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Discriminative Entity-Aware Language Model for Virtual Assistants
High-quality automatic speech recognition (ASR) is essential for virtual assistants (VAs) to work well.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Error-driven Pruning of Language Models for Virtual Assistants
We customize entropy pruning by allowing for a keep list of infrequent n-grams that require a more relaxed pruning threshold, and propose three methods to construct the keep list.
Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants
We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries.
Connecting and Comparing Language Model Interpolation Techniques
In this work, we uncover a theoretical connection between two language model interpolation techniques, count merging and Bayesian interpolation.
