Enriching Under-Represented Named-Entities To Improve Speech Recognition Performance

10/23/2020
by   Tingzhi Mao, et al.
0

Automatic speech recognition (ASR) for under-represented named-entity (UR-NE) is challenging due to such named-entities (NE) have insufficient instances and poor contextual coverage in the training data to learn reliable estimates and representations. In this paper, we propose approaches to enriching UR-NEs to improve speech recognition performance. Specifically, our first priority is to ensure those UR-NEs to appear in the word lattice if there is any. To this end, we make exemplar utterances for those UR-NEs according to their categories (e.g. location, person, organization, etc.), ending up with an improved language model (LM) that boosts the UR-NE occurrence in the word lattice. With more UR-NEs appearing in the lattice, we then boost the recognition performance through lattice rescoring methods. We first enrich the representations of UR-NEs in a pre-trained recurrent neural network LM (RNNLM) by borrowing the embedding representations of the rich-represented NEs (RR-NEs), yielding the lattices that statistically favor the UR-NEs. Finally, we directly boost the likelihood scores of the utterances containing UR-NEs and gain further performance improvement.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

05/18/2020

Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems

In this paper, we present a series of complementary approaches to improv...
07/10/2020

Class LM and word mapping for contextual biasing in End-to-End ASR

In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid i...
05/15/2020

Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model

Videos uploaded on social media are often accompanied with textual descr...
06/21/2021

A Discriminative Entity-Aware Language Model for Virtual Assistants

High-quality automatic speech recognition (ASR) is essential for virtual...
10/26/2018

A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition

Deep Feedforward Sequential Memory Network (DFSMN) has shown superior pe...
10/30/2018

Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation

The standard approach to mitigate errors made by an automatic speech rec...
05/26/2020

Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants

We focus on improving the effectiveness of a Virtual Assistant (VA) in r...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.