5 papers
Objective Intelligibility Prediction Using Distance Metrics on Speech Foundation Model Representations
Lyonel Behringer, Andreas Brendel
High-dimensional representations of pretrained speech foundation models have proven beneficial for objective speech quality and intelligibility prediction. While existing work on n…
Assessing the Impact of Noise and Speech Enhancement on the Intelligibility of Speech Codecs
Lyonel Behringer, Anna Leschanowsky, Anjana Rajasekhar +2
Preserving speech intelligibility is a minimum requirement for speech codecs in communication. Recently, very low-bitrate neural codecs have gained interest for replacing classical…
Benchmarking Neural Speech Codec Intelligibility with SITool
Anna Leschanowsky, Kishor Kayyar Lakshminarayana, Anjana Rajasekhar +4
Speech intelligibility assessment is essential for evaluating neural speech codecs, yet most evaluation efforts focus on overall quality rather than intelligibility. Only a few pub…
Meta Learning Text-to-Speech Synthesis in over 7000 Languages
Florian Lux, Sarina Meyer, Lyonel Behringer +5
In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack s…
Neural Speech Coding for Real-time Communications using Constant Bitrate Scalar Quantization
Andreas Brendel, Nicola Pia, Kishan Gupta +3
Neural audio coding has emerged as a vivid research direction by promising good audio quality at very low bitrates unachievable by classical coding techniques. Here, end-to-end tra…