output
19942026
most citedGender bias and stereotypes in Large Language Models

361 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2020 · eess.ASShow all

8 papers · 2 filters

eess.AS2020★ 79 cited

Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

Peter Bell, Joachim Fainberg, Ondrej Klejch +3

We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-…

eess.AS2020★ 19 cited

Online Automatic Speech Recognition with Listen, Attend and Spell Model

Roger Hsiao, Dogan Can, Tim Ng +2

The Listen, Attend and Spell (LAS) model and other attention-based automatic speech recognition (ASR) models have known limitations when operated in a fully online mode. In this pa…

eess.AS2020★ 2 cited

Acoustic Neighbor Embeddings

Woojay Jeon

This paper proposes a novel acoustic word embedding called Acoustic Neighbor Embeddings where speech or text of arbitrary length are mapped to a vector space of fixed, reduced dime…

eess.AS2020★ 2 cited

Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis

Ting-Yao Hu, Ashish Shrivastava, Oncel Tuzel +1

We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, suc…

eess.AS2020

Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions

Vasudha Kowtha, Vikramjit Mitra, Chris Bartels +5

Emotion plays an essential role in human-to-human communication, enabling us to convey feelings such as happiness, frustration, and sincerity. While modern speech technologies rely…

eess.AS2020★ 26 cited

Multi-task Learning for Speaker Verification and Voice Trigger Detection

Siddharth Sigtia, Erik Marchi, Sachin Kajarekar +2

Automatic speech transcription and speaker recognition are usually treated as separate tasks even though they are interdependent. In this study, we investigate training a single ne…

eess.AS2020

Lattice-based Improvements for Voice Triggering Using Graph Neural Networks

Pranay Dighe, Saurabh Adya, Nuoyu Li +6

Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect o…

eess.AS2020★ 16 cited

Multi-task Learning for Voice Trigger Detection

Siddharth Sigtia, Pascal Clark, Rob Haynes +2

We describe the design of a voice trigger detection system for smart speakers. In this study, we address two major challenges. The first is that the detectors are deployed in compl…