20 citations · 38 across the 9 of their papers we have counts for
9 papers
Data Augmentation for Pathological Speech Enhancement
Mingchi Hou, Enno Hermann, Ina Kodrasi
The performance of state-of-the-art speech enhancement (SE) models considerably degrades for pathological speech due to atypical acoustic characteristics and limited data availabil…
Children's Voice Privacy: First Steps And Emerging Challenges
Ajinkya Kulkarni, Francisco Teixeira, Enno Hermann +3
Children are one of the most under-represented groups in speech technologies, as well as one of the most vulnerable in terms of privacy. Despite this, anonymization techniques targ…
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
Karl El Hajal, Enno Hermann, Sevada Hovsepyan +1
Automatic speech recognition (ASR) systems struggle with dysarthric speech due to high inter-speaker variability and slow speaking rates. To address this, we explore dysarthric-to-…
Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR
Karl El Hajal, Enno Hermann, Ajinkya Kulkarni +1
Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mis…
kNN Retrieval for Simple and Effective Zero-Shot Multi-speaker Text-to-Speech
Karl El Hajal, Ajinkya Kulkarni, Enno Hermann +1
While recent zero-shot multi-speaker text-to-speech (TTS) models achieve impressive results, they typically rely on extensive transcribed speech datasets from numerous speakers and…
Towards interfacing large language models with ASR systems using confidence measures and prompting
Maryam Naderi, Enno Hermann, Alexandre Nanchen +2
As large language models (LLMs) grow in parameter size and capabilities, such as interaction through prompting, they open up new ways of interfacing with automatic speech recogniti…
An Objective Evaluation Framework for Pathological Speech Synthesis
Bence Mark Halpern, Julian Fritsch, Enno Hermann +3
The development of pathological speech systems is currently hindered by the lack of a standardised objective evaluation framework. In this work, (1) we utilise existing detection a…
Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages
Enno Hermann, Herman Kamper, Sharon Goldwater
Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such…
Multilingual bottleneck features for subword modeling in zero-resource languages
Enno Hermann, Sharon Goldwater
How can we effectively develop speech technology for languages where no transcribed data is available? Many existing approaches use no annotated resources at all, yet it makes sens…