Beyond Deep Learning: Speech Segmentation and Phone Classification with Neural Assemblies
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| Format: | Preprint |
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2026
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| _version_ | 1866911524395679744 |
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| author | Adelson, Trevor Sethu, Vidhyasaharan Dang, Ting |
| author_facet | Adelson, Trevor Sethu, Vidhyasaharan Dang, Ting |
| contents | Deep learning dominates speech processing but relies on massive datasets, global backpropagation-guided weight updates, and produces entangled representations. Assembly Calculus (AC), which models sparse neuronal assemblies via Hebbian plasticity and winner-take-all competition, offers a biologically grounded alternative, yet prior work focused on discrete symbolic inputs. We introduce an AC-based speech processing framework that operates directly on continuous speech by combining three key contributions:(i) neural encoding that converts speech into assembly-compatible spike patterns using probabilistic mel binarisation and population-coded MFCCs; (ii) a multi-area architecture organising assemblies across hierarchical timescales and classes; and (iii) cross-area update schemes for downstream tasks. Applied to two core tasks of boundary detection and segment classification, our framework detects phone (F1=0.69) and word (F1=0.61) boundaries without any weight training, and achieves 47.5% and 45.1% accuracy on phone and command recognition. These results show that AC-based dynamical systems are a viable alternative to deep learning for speech processing. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_16923 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Deep Learning: Speech Segmentation and Phone Classification with Neural Assemblies Adelson, Trevor Sethu, Vidhyasaharan Dang, Ting Audio and Speech Processing Sound I.2.0; I.2.6; I.2.7 Deep learning dominates speech processing but relies on massive datasets, global backpropagation-guided weight updates, and produces entangled representations. Assembly Calculus (AC), which models sparse neuronal assemblies via Hebbian plasticity and winner-take-all competition, offers a biologically grounded alternative, yet prior work focused on discrete symbolic inputs. We introduce an AC-based speech processing framework that operates directly on continuous speech by combining three key contributions:(i) neural encoding that converts speech into assembly-compatible spike patterns using probabilistic mel binarisation and population-coded MFCCs; (ii) a multi-area architecture organising assemblies across hierarchical timescales and classes; and (iii) cross-area update schemes for downstream tasks. Applied to two core tasks of boundary detection and segment classification, our framework detects phone (F1=0.69) and word (F1=0.61) boundaries without any weight training, and achieves 47.5% and 45.1% accuracy on phone and command recognition. These results show that AC-based dynamical systems are a viable alternative to deep learning for speech processing. |
| title | Beyond Deep Learning: Speech Segmentation and Phone Classification with Neural Assemblies |
| topic | Audio and Speech Processing Sound I.2.0; I.2.6; I.2.7 |
| url | https://arxiv.org/abs/2603.16923 |