1 citations · 1 across the 8 of their papers we have counts for
8 papers
Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results
Ilse Huisman, Rares Popa, Yuanyuan Zhang +1
Humans are often considered to be the best listeners and seen as the upper-bound performance of automatic speech recognition (ASR) systems. We present a preliminary comparison of t…
Comparing Human and Automatic Recognition of Dutch Dysarthric Continuous Speech: A Case Study
Yuanyuan Zhang, Dimme de Groot, Jorge Martinez +1
In our goal to develop personalised dysarthric speech recognition (DSR) models, this study compared the recognition performances of human listeners and those of three state-of-the-…
A Semi-spontaneous Dutch Speech Dataset for Speech Enhancement and Speech Recognition
Dimme de Groot, Yuanyuan Zhang, Jorge Martinez +1
We present DRES: a 1.5-hour Dutch realistic elicited (semi-spontaneous) speech dataset from 80 speakers recorded in noisy, public indoor environments. DRES was designed as a test s…
Objective and Subjective Evaluation of Diffusion-Based Speech Enhancement for Dysarthric Speech
Dimme de Groot, Tanvina Patel, Devendra Kayande +2
Dysarthric speech poses significant challenges for automatic speech recognition (ASR) systems due to its high variability and reduced intelligibility. In this work we explore the u…
How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures
Tanvina Patel, Wiebke Hutiri, Aaron Yi Ding +1
There is increasingly more evidence that automatic speech recognition (ASR) systems are biased against different speakers and speaker groups, e.g., due to gender, age, or accent. R…
Loudspeaker Beamforming to Enhance Speech Recognition Performance of Voice Driven Applications
Dimme de Groot, Baturalp Karslioglu, Odette Scharenborg +1
In this paper we propose a robust loudspeaker beamforming algorithm which is used to enhance the performance of voice driven applications in scenarios where the loudspeakers introd…
Good practices for evaluation of machine learning systems
Luciana Ferrer, Odette Scharenborg, Tom Bäckström
Many development decisions affect the results obtained from ML experiments: training data, features, model architecture, hyperparameters, test data, etc. Among these aspects, argua…
Self-supervised Speech Representations Still Struggle with African American Vernacular English
Kalvin Chang, Yi-Hui Chou, Jiatong Shi +4
Underperformance of ASR systems for speakers of African American Vernacular English (AAVE) and other marginalized language varieties is a well-documented phenomenon, and one that r…