31 citations · 39 across the 15 of their papers we have counts for
15 papers
FlowW2N: Whispered-to-Normal Speech Conversion via Flow-Matching
Fabian Ritter-Gutierrez, Md Asif Jalal, Pablo Peso Parada +5
Whispered-to-normal (W2N) speech conversion aims to reconstruct missing phonation from whispered input while preserving content and speaker identity. This task is challenging due t…
Robust Target Speaker Diarization and Separation via Augmented Speaker Embedding Sampling
Md Asif Jalal, Luca Remaggi, Vasileios Moschopoulos +7
Traditional speech separation and speaker diarization approaches rely on prior knowledge of target speakers or a predetermined number of participants in audio signals. To address t…
Diffusion based Text-to-Music Generation with Global and Local Text based Conditioning
Jisi Zhang, Pablo Peso Parada, Md Asif Jalal +1
Diffusion based Text-To-Music (TTM) models generate music corresponding to text descriptions. Typically UNet based diffusion models condition on text embeddings generated from a pr…
persoDA: Personalized Data Augmentation for Personalized ASR
Pablo Peso Parada, Spyros Fontalis, Md Asif Jalal +6
Data augmentation (DA) is ubiquitously used in training of Automatic Speech Recognition (ASR) models. DA offers increased data variability, robustness and generalization against di…
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…
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition
Anna Ollerenshaw, Md Asif Jalal, Rosanna Milner +1
Speech emotion recognition (SER) is vital for obtaining emotional intelligence and understanding the contextual meaning of speech. Variations of consonant-vowel (CV) phonemic bound…
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation
Rehan Ahmad, Md Asif Jalal, Muhammad Umar Farooq +2
Knowledge distillation has widely been used for model compression and domain adaptation for speech applications. In the presence of multiple teachers, knowledge can easily be trans…
Probing Statistical Representations For End-To-End ASR
Anna Ollerenshaw, Md Asif Jalal, Thomas Hain
End-to-End automatic speech recognition (ASR) models aim to learn a generalised speech representation to perform recognition. In this domain there is little research to analyse int…
Dynamic Kernels and Channel Attention for Low Resource Speaker Verification
Anna Ollerenshaw, Md Asif Jalal, Thomas Hain
State-of-the-art speaker verification frameworks have typically focused on developing models with increasingly deeper (more layers) and wider (number of channels) models to improve…
A cross-corpus study on speech emotion recognition
Rosanna Milner, Md Asif Jalal, Raymond W. M. Ng +1
For speech emotion datasets, it has been difficult to acquire large quantities of reliable data and acted emotions may be over the top compared to less expressive emotions displaye…
Insights on Neural Representations for End-to-End Speech Recognition
Anna Ollerenshaw, Md Asif Jalal, Thomas Hain
End-to-end automatic speech recognition (ASR) models aim to learn a generalised speech representation. However, there are limited tools available to understand the internal functio…
Investigating Deep Neural Structures and their Interpretability in the Domain of Voice Conversion
Samuel J. Broughton, Md Asif Jalal, Roger K. Moore
Generative Adversarial Networks (GANs) are machine learning networks based around creating synthetic data. Voice Conversion (VC) is a subset of voice translation that involves tran…