113 citations · 157 across the 18 of their papers we have counts for
19 papers
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR
Ruchao Fan, Yunzheng Zhu, Jinhan Wang +1
Recently, self-supervised learning (SSL) from unlabelled speech data has gained increased attention in the automatic speech recognition (ASR) community. Typical SSL methods include…
Learning from human perception to improve automatic speaker verification in style-mismatched conditions
Amber Afshan, Abeer Alwan
Our prior experiments show that humans and machines seem to employ different approaches to speaker discrimination, especially in the presence of speaking style variability. The exp…
Attention-based conditioning methods using variable frame rate for style-robust speaker verification
Amber Afshan, Abeer Alwan
We propose an approach to extract speaker embeddings that are robust to speaking style variations in text-independent speaker verification. Typically, speaker embedding extraction…
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR
Ruchao Fan, Abeer Alwan
Self-supervised learning (SSL) in the pretraining stage using un-annotated speech data has been successful in low-resource automatic speech recognition (ASR) tasks. However, models…
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition
Yunzheng Zhu, Ruchao Fan, Abeer Alwan
Children's automatic speech recognition (ASR) is always difficult due to, in part, the data scarcity problem, especially for kindergarten-aged kids. When data are scarce, the model…
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects
Alexander Johnson, Ruchao Fan, Robin Morris +1
This paper proposes a novel linear prediction coding-based data aug-mentation method for children's low and zero resource dialect ASR. The data augmentation procedure consists of p…
FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals
Vijay Ravi, Jinhan Wang, Jonathan Flint +1
In this paper, a data augmentation method is proposed for depression detection from speech signals. Samples for data augmentation were created by changing the frame-width and the f…
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition
Ruchao Fan, Wei Chu, Peng Chang +2
Non-autoregressive mechanisms can significantly decrease inference time for speech transformers, especially when the single step variant is applied. Previous work on CTC alignment-…
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System
Jinhan Wang, Yunzheng Zhu, Ruchao Fan +2
This paper describes the SPAPL system for the INTERSPEECH 2021 Challenge: Shared Task on Automatic Speech Recognition for Non-Native Children's Speech in German. ~ 5 hours of trans…
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition
Gary Yeung, Ruchao Fan, Abeer Alwan
Automatic speech recognition (ASR) systems for young children are needed due to the importance of age-appropriate educational technology. Because of the lack of publicly available…
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR
Ruchao Fan, Amber Afshan, Abeer Alwan
We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). An obstacle to improving child ASR is the sca…
Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop
Matthew Marge, Carol Espy-Wilson, Nigel Ward
With robotics rapidly advancing, more effective human-robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be pa…
Analysis of Disfluency in Children's Speech
Trang Tran, Morgan Tinkler, Gary Yeung +2
Disfluencies are prevalent in spontaneous speech, as shown in many studies of adult speech. Less is understood about children's speech, especially in pre-school children who are st…
Speaker discrimination in humans and machines: Effects of speaking style variability
Amber Afshan, Jody Kreiman, Abeer Alwan
Does speaking style variation affect humans' ability to distinguish individuals from their voices? How do humans compare with automatic systems designed to discriminate between voi…
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification
Amber Afshan, Jinxi Guo, Soo Jin Park +3
The effects of speaking-style variability on automatic speaker verification were investigated using the UCLA Speaker Variability database which comprises multiple speaking styles p…
Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification
Vijay Ravi, Ruchao Fan, Amber Afshan +2
In this paper, we propose a novel way of addressing text-dependent automatic speaker verification (TD-ASV) by using a shared-encoder with task-specific decoders. An autoregressive…
Glottal Source Processing: from Analysis to Applications
Thomas Drugman, Paavo Alku, Abeer Alwan +1
The great majority of current voice technology applications relies on acoustic features characterizing the vocal tract response, such as the widely used MFCC of LPC parameters. Non…
Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics
Thomas Drugman, Abeer Alwan
This paper focuses on the problem of pitch tracking in noisy conditions. A method using harmonic information in the residual signal is presented. The proposed criterion is used bot…
Deep neural network based i-vector mapping for speaker verification using short utterances
Jinxi Guo, Ning Xu, Kailun Qian +4
Text-independent speaker recognition using short utterances is a highly challenging task due to the large variation and content mismatch between short utterances. I-vector based sy…