15 citations · 23 across the 6 of their papers we have counts for
6 papers
What Counts as Real? Speech Restoration and Voice Quality Conversion Pose New Challenges to Deepfake Detection
Shree Harsha Bokkahalli Satish, Harm Lameris, Joakim Gustafson +1
Audio anti-spoofing systems are typically trained to assign one authenticity label to an entire speech utterance. This formulation becomes under-specified for transformations where…
Lost in Phonation: Voice Quality Variation as an Evaluation Dimension for Speech Foundation Models
Harm Lameris, Shree Harsha Bokkahalli Satish, Joakim Gustafson +1
Recent advances in Speech Foundation Models (SFMs) enable direct processing of raw audio, allowing models to respond to subtle paralinguistic variation. However, how these models i…
Speak Your Mind: The Speech Continuation Task as a Probe of Voice-Based Model Bias
Shree Harsha Bokkahalli Satish, Harm Lameris, Olivier Perrotin +2
Speech Continuation (SC) is the task of generating a coherent extension of a spoken prompt while preserving both semantic context and speaker identity. Because SC is constrained to…
Should you use a probabilistic duration model in TTS? Probably! Especially for spontaneous speech
Shivam Mehta, Harm Lameris, Rajiv Punmiya +3
Converting input symbols to output audio in TTS requires modelling the durations of speech sounds. Leading non-autoregressive (NAR) TTS models treat duration modelling as a regress…
Prosody-controllable spontaneous TTS with neural HMMs
Harm Lameris, Shivam Mehta, Gustav Eje Henter +2
Spontaneous speech has many affective and pragmatic functions that are interesting and challenging to model in TTS. However, the presence of reduced articulation, fillers, repetiti…
OverFlow: Putting flows on top of neural transducers for better TTS
Shivam Mehta, Ambika Kirkland, Harm Lameris +3
Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech. They combine the best features of classic statistical speech syn…