Papers by Baris Kasikci
LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation (2025.emnlp-main)
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| Challenge: | Modern automatic speech recognition systems rely on encoder-decoder architectures and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. |
| Approach: | They propose a low-rank compression scheme for ASR encoders that leverages the strong low-ranked properties observed in intermediate activations and approximates linear transformations with a chain of low-Rank matrix multiplications. |
| Outcome: | The proposed method reduces inference costs while maintaining transcription accuracy while preserving low-rank properties observed in intermediate activations. |