Evidence map›Paper›PMID 42179594›Full record

ArticleACS omega2026

Programmable Speech Recognition Based on Cu/CuBiSe

Shan Jin, Meiyan Wang, Zhengzhou Wang, Anan Guo, Hao Luo, Zukang Zhu, Han Xu, Haoyang Peng, Xianli Su, Jinsong Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Shan JinState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Meiyan WangState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Zhengzhou WangState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Anan GuoState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Hao LuoState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Zukang ZhuState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Han XuState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Haoyang PengState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.
Xianli SuState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.ORCID https://orcid.org/0000-0003-4428-6461
Jinsong WuState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China.ORCID https://orcid.org/0000-0002-7305-7927

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional speech recognition systems heavily rely on manually designed acoustic features and are limited by the inherent constraints of models that overlook continuity and dynamic characteristics. This leads to reduced recognition accuracy and high energy consumption. Recurrent neural networks (RNNs) have shown significant potential in temporal-data processing owing to their strong autonomous learning capabilities and numerous adjustable parameters. Here, we present the design of a high-performance Cu/CuBiSe

Identifiers

PMID42179594
PMCPMC13191699

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.