The paper introduces the Offline AI Modules workstream, which provides a voice‑first offline architecture, a low‑cost hardware reference bill of materials, and a reproducible quantization and benchmarking pipeline for instruction‑tuned language models in the 2‑5B parameter range. It evaluates three models across four quantization formats on two hardware tiers—NVIDIA Jetson Orin NX and Raspberry Pi5—measuring deployment metrics and multilingual quality. The key result is that Q4_K_M quantization offers the best size‑to‑quality trade‑off, enabling high decode throughput and strong topic classification accuracy while staying within memory limits on both tiers.
By Sunday Afariogun, Odunolaoluwa Jenrola, Zeinab Nezami
The paper evaluates a 305 M‑parameter audio encoder shared by Gemma 4 and EmbeddingGemma 2 for Saudi and Gulf Arabic speech recognition without altering its weights. Two approaches are compared: a frozen system that trains only a 22.6 M‑parameter CTC reader, and an adapted system that adds 3.1 M low‑rank adapters, a 41.5 M‑parameter hybrid CTC/attention reader, and joint decoding with a sub‑word language model. The adapted system matches or surpasses a 0.6 B‑parameter fine‑tuned recogniser on several datasets, while the frozen system remains competitive in character error rate and runs three‑quarters faster on CPU with greedy decoding.
By Amr Abdelkhalik
The study investigates how two computational dimensions—model depth and refinement steps—affect intelligibility and speaker identity in masked-diffusion text‑to‑speech systems. Experiments with 15 models (19–133 M parameters) and up to 16 refinement steps show that refinement improves intelligibility more than identity, with a 1.86× asymmetry that persists even after retraining. Best‑of‑K search can recover identity when refinement fails, and analysis indicates that depth and steps target distinct bottlenecks, requiring separate optimization.
By Nityanand Mathur, Hamees Sayed, Ayush Pratap Singh
arXiv:2610.35272v1 Announce Type: cross
Abstract: A transcriber listening through one microphone also hears everyone near it. We train causal voice isolators: masking transformers of 140M, 243M and 3...
By Husein Zolkepli
The paper introduces WaxalNet, a benchmark and adaptation study for spontaneous speech recognition across 19 African languages using the WAXAL corpus. It evaluates zero‑shot and fine‑tuned performance of several multilingual foundation models (Whisper, MMS, Omnilingual ASR, SeamlessM4T‑v2), showing that fine‑tuning can substantially reduce word error rates and that LLM decoders generally outperform CTC decoders when the encoder is shared. The authors also demonstrate that a single multilingual model can serve all languages with only a modest WER penalty and provide a native‑speaker audit of failure modes.
By Victor Tolulope Olufemi, Oreoluwa Babatunde, Ramsey Njema, Bolarinwa Gbotemi, Wanchi Lucia Yen, John Uzodinma, Sunday Ajayi, Oluwademilade Williams, Kausar Moshood, Innocent Elendu Anyaele, Akebert Arefaine, Candace Hunzwi, Wongel Dawit Daniel, Emmilly Namuganga, Cleophas Kadima, Athanase Bahizire, Onitsiky Ranaivoson, Emmanuel Aaron, Nicholaus Ladislaus, Idris Muhammed, Jonathan Enoch Simenya, Martin Koome, Matewos Tegete Endaylalu, Peter Ifeoluwa Adeyemo, Hondi Prisca Birindwa, Ukachi Agnes Eze-Mbey, Yacoba Oduro-Yeboah, Pericles Adjovi, Mikel K. Ngueajio, Toluwani Aremu, Prasenjit Mitra
arXiv:2602. 04101v2 Announce Type: replace Abstract: We present Interfaze, a native hybrid model that fuses task-specific deep neural networks (CNNs and DNNs) directly into a transformer decoder through a shared embedding space.
By Harsha Vardhan Khurdula, Vineet Agarwal, Yoeven D Khemlani
arXiv:2606. 24169v1 Announce Type: new Abstract: Adapting a streaming speech recognition model to a new language requires choosing between two plausible warm starts: a multilingual (ML) encoder or an English-only (EN) encoder.
By Nenad Banfic
arXiv:2608. 20210v1 Announce Type: cross Abstract: Small language models are usually built like large ones and then squeezed onto a CPU afterwards.
By Christos Koutsiaris
arXiv:2609.14817v1 Announce Type: new
Abstract: In Yor\`ub\'a, pitch alone separates \d{o}k\d{o} (husband, Mid), \d{o}k\d{\`o} (vehicle, Low), and \d{o}k\d{\'o} (hoe, High) -- the diacritics ARE the...
By Moses Daudu, Adeola Enitan Bamidele, Honor-Jesus Bezaleel
The paper presents a new evaluation protocol for post‑training quantization of speech language models that separates lexical output, transcript‑insufficient endpoints, and packed implementations. In a Qwen2‑Audio case study, a 6‑bit allocation selected for translation improves chrF scores but degrades emotion recognition, while uniform and front‑layer controls perform better on emotion tasks. Similar patterns hold at 7 bits, and a 4‑bit study shows consistent emotion deficits across all low‑bit allocations, with no advantage for the selected scheme. The study highlights a precision‑dependent mismatch between lexical output, waveform‑dependent behavior, and nominal precision, without claiming a general failure of low‑bit models or a deployment benefit for the selected allocation.
By Mengzhe Geng, Jinxi Jin, Junhao Xu
InteractionBench is a benchmark that evaluates real‑time voice systems on four dimensions—conversational timing, spoken‑task accuracy, whispering and volume, and groundedness—using the same recorded user audio across thirteen scenarios. It tests seven systems (GPT‑Live, GPT‑Realtime, Gemini Live, Moshi, PersonaPlex, Freeze‑Omni, and a turn‑based cascade) over 1,519 runs, revealing correlations between timing and task accuracy and highlighting differences in whispering, grammar correction, and groundedness performance. The study also notes protocol variations and the impact of network delay on timing scores.
By Richard Yucheng He, Chen Xu, Yihang Liu, Tairan Chen, Baodong Cao
Small language models are usually built like large ones and then squeezed onto a CPU afterwards. We did the opposite: we fixed the target first, one user, one token at a time, 4-bit weights, ordinary CPU, and chose the architecture to suit it.