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New speech parsing architecture reduces parameters without performance loss

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a simpler end-to-end architecture for speech parsing that removes intermediate neural network units, reducing parameters by 12% while maintaining or improving performance. This new approach demonstrates that intermediate NN units are primarily beneficial for bridging the representational gap when a pre-trained encoder is frozen. The study provides a comprehensive evaluation on French, and the lower-resource languages Slovenian and Naija, also examining the impact of training data size and pre-trained speech encoder layers. AI

IMPACT This research offers a more efficient approach to speech parsing, potentially reducing computational costs and improving performance for low-resource languages.

RANK_REASON Academic paper on model architecture and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New speech parsing architecture reduces parameters without performance loss

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Academic paper on model architecture and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Minnie Kabra, Benjamin Lecouteux, Maximin Coavoux ·

    When Can You Prune Your Network? A Study of Intermediate Neurons in Multilingual Speech Parsing

    arXiv:2610.11520v1 Announce Type: new Abstract: End-to-end speech parsing, a task recently proposed, consists in predicting both the transcription and the syntactic tree for a spoken utterance. Existing architectures for speech parsing often utilise intermediate neural networks. …

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