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Daniel Korzekwa

6 accepted papers

2026

Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control

Ali Taghibakhshi, Ruisi Cai, Saurav Muralidharan, Sharath Turuvekere Sreenivas, Ameya Mahabaleshwarkar, Marcin Chochowski +10
ICML 2026poster

Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiring separate training runs for each model in the family. In this paper, we introduce Star Elastic, a novel LLM post-training method that adds N neste…

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2025

Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning

Ali Taghibakhshi, Sharath Turuvekere Sreenivas, Saurav Muralidharan, Marcin Chochowski, Yashaswi Karnati, Raviraj Bhuminand Joshi +12
NeurIPS 2025poster

Hybrid language models that combine Attention and State Space Models (SSMs) have been shown to achieve state-of-the-art accuracy and runtime performance. Recent work has also demonstrated that applying pruning and distillation to Attention-only models yields smaller, more accurate models at a fracti…

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2023

AE-Flow: Autoencoder Normalizing Flow

Jakub Mosinski, Piotr Bilinski, Thomas Merritt, Abdelhamid Ezzerg, Daniel Korzekwa
ICASSP 2023accepted

Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsupervised generative models. In this paper, we introduce supervision to the training process of normalizing flows, withou…

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2022

Text-Free Non-Parallel Many-To-Many Voice Conversion Using Normalising Flow

Thomas Merritt, Abdelhamid Ezzerg, Piotr Bilinski, Magdalena Proszewska, Kamil Pokora, Roberto Barra-Chicote +1
ICASSP 2022accepted

Non-parallel voice conversion (VC) is typically achieved using lossy representations of the source speech. However, ensuring only speaker identity information is dropped whilst all other information from the source speech is retained is a large challenge. This is particularly challenging in the scen…

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2021

Mispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling

Daniel Korzekwa, Jaime Lorenzo-Trueba, Szymon Zaporowski, Shira Calamaro, Thomas Drugman, Bozena Kostek
ICASSP 2021accepted

A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciation of a native speaker. This approach makes two simplifying assumptions: a) phonemes can be recognized from speech with h…

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2021

Universal Neural Vocoding with Parallel Wavenet

Yunlong Jiao, Adam Gabrys, Georgi Tinchev, Bartosz Putrycz, Daniel Korzekwa, Viacheslav Klimkov
ICASSP 2021accepted

We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder. Our universal vocoder offers real-time high-quality speech synthesis on a wide range of use cases. We tested it on 43 internal speakers of diverse age and gender, speaking 2…

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