8 citations · 22 across the 8 of their papers we have counts for
8 papers
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…
As Biased as You Measure: Methodological Pitfalls of Bias Evaluations in Speaker Verification Research
Wiebke Hutiri, Tanvina Patel, Aaron Yi Ding +1
Detecting and mitigating bias in speaker verification systems is important, as datasets, processing choices and algorithms can lead to performance differences that systematically f…
Improving child speech recognition with augmented child-like speech
Yuanyuan Zhang, Zhengjun Yue, Tanvina Patel +1
State-of-the-art ASRs show suboptimal performance for child speech. The scarcity of child speech limits the development of child speech recognition (CSR). Therefore, we studied chi…
Exploring data augmentation in bias mitigation against non-native-accented speech
Yuanyuan Zhang, Aaricia Herygers, Tanvina Patel +2
Automatic speech recognition (ASR) should serve every speaker, not only the majority ``standard'' speakers of a language. In order to build inclusive ASR, mitigating the bias again…
Improving Whispered Speech Recognition Performance using Pseudo-whispered based Data Augmentation
Zhaofeng Lin, Tanvina Patel, Odette Scharenborg
Whispering is a distinct form of speech known for its soft, breathy, and hushed characteristics, often used for private communication. The acoustic characteristics of whispered spe…
Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems
Tanvina Patel, Odette Scharenborg
Speech technology has improved greatly for norm speakers, i.e., adult native speakers of a language without speech impediments or strong accents. However, non-norm or diverse speak…
Predicting within and across language phoneme recognition performance of self-supervised learning speech pre-trained models
Hang Ji, Tanvina Patel, Odette Scharenborg
In this work, we analyzed and compared speech representations extracted from different frozen self-supervised learning (SSL) speech pre-trained models on their ability to capture a…