1 citations · 1 across the 6 of their papers we have counts for
6 papers
Exploring compressibility of transformer based text-to-music (TTM) models
Vasileios Moschopoulos, Thanasis Kotsiopoulos, Pablo Peso Parada +7
State-of-the art Text-To-Music (TTM) generative AI models are large and require desktop or server class compute, making them infeasible for deployment on mobile phones. This paper…
Locality enhanced dynamic biasing and sampling strategies for contextual ASR
Md Asif Jalal, Pablo Peso Parada, George Pavlidis +8
Automatic Speech Recognition (ASR) still face challenges when recognizing time-variant rare-phrases. Contextual biasing (CB) modules bias ASR model towards such contextually-releva…
Consistency Based Unsupervised Self-training For ASR Personalisation
Jisi Zhang, Vandana Rajan, Haaris Mehmood +7
On-device Automatic Speech Recognition (ASR) models trained on speech data of a large population might underperform for individuals unseen during training. This is due to a domain…
On-Device Speaker Anonymization of Acoustic Embeddings for ASR based onFlexible Location Gradient Reversal Layer
Md Asif Jalal, Pablo Peso Parada, Jisi Zhang +5
Smart devices serviced by large-scale AI models necessitates user data transfer to the cloud for inference. For speech applications, this means transferring private user informatio…
Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics
Umberto Michieli, Pablo Peso Parada, Mete Ozay
Keyword Spotting (KWS) models on embedded devices should adapt fast to new user-defined words without forgetting previous ones. Embedded devices have limited storage and computatio…
pMCT: Patched Multi-Condition Training for Robust Speech Recognition
Pablo Peso Parada, Agnieszka Dobrowolska, Karthikeyan Saravanan +1
We propose a novel Patched Multi-Condition Training (pMCT) method for robust Automatic Speech Recognition (ASR). pMCT employs Multi-condition Audio Modification and Patching (MAMP)…