research
∙
11/04/2022
Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech
Stuttering is a speech disorder where the natural flow of speech is inte...
share
research
∙
10/22/2022
Guided contrastive self-supervised pre-training for automatic speech recognition
Contrastive Predictive Coding (CPC) is a representation learning method ...
research
∙
07/16/2022
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation
We present an approach to reduce the performance disparity between geogr...
research
∙
07/15/2022
Adversarial Reweighting for Speaker Verification Fairness
We address performance fairness for speaker verification using the adver...
research
∙
02/23/2022
Improving fairness in speaker verification via Group-adapted Fusion Network
Modern speaker verification models use deep neural networks to encode ut...
research
∙
12/01/2021
Investigation of Training Label Error Impact on RNN-T
In this paper, we propose an approach to quantitatively analyze impacts ...
research
∙
06/14/2021
SynthASR: Unlocking Synthetic Data for Speech Recognition
End-to-end (E2E) automatic speech recognition (ASR) models have recently...
research
∙
06/14/2021
CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition
We propose a simple yet effective method to compress an RNN-Transducer (...
research
∙
06/11/2021
Scaling Laws for Acoustic Models
There is a recent trend in machine learning to increase model quality by...
research
∙
06/10/2021
Improving multi-speaker TTS prosody variance with a residual encoder and normalizing flows
Text-to-speech systems recently achieved almost indistinguishable qualit...
research
∙
05/14/2021
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End
Comprehending the overall intent of an utterance helps a listener recogn...
research
∙
03/09/2021
Wav2vec-C: A Self-supervised Model for Speech Representation Learning
Wav2vec-C introduces a novel representation learning technique combining...
research
∙
02/12/2021
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding
Spoken language understanding (SLU) systems extract transcriptions, as w...
research
∙
12/29/2020
Detection of Lexical Stress Errors in Non-native (L2) English with Data Augmentation and Attention
This paper describes two novel complementary techniques that improve the...
research
∙
07/27/2020
Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition
In this work, we propose a novel and efficient minimum word error rate (...
research
∙
11/28/2017
Acoustic-To-Word Model Without OOV
Recently, the acoustic-to-word model based on the Connectionist Temporal...
research
∙
08/29/2017
Comparing Human and Machine Errors in Conversational Speech Transcription
Recent work in automatic recognition of conversational telephone speech ...
research
∙
07/21/2017
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
Unsupervised single-channel overlapped speech recognition is one of the ...
research
∙
02/19/2016