FusionNet: A parallel deep learning model for speech recognition with feature clustering Article Swipe
Ramteke, Revati Harichandra
,
Rathod, Seema B
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17862065
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17862065
FusionNet is a parallel, hybrid deep-learning framework engineered for next-generation speech recognition and on-device speech-to-text processing. The system is implemented as an Android application (Java/XML) and integrated with Firebase Realtime Database to support secure, user-centric data management. Audio input undergoes a multi-stage preprocessing pipeline where MFCC, spectral, and temporal features are extracted and clustered using K-Means to group acoustically similar speech segments. These clustered representations are simultaneously processed through a dual-branch architecture: a Convolutional Neural Network (CNN) that learns spectral signatures and a Bidirectional Long Short-Term Memory (BiLSTM) network that models temporal dependencies. The fused embeddings are then classified using a Random Forest classifier, improving prediction stability in noisy or accent-variable conditions. To enhance semantic clarity, an NLP engine supported by a generative AI model refines the raw transcriptions, corrects contextual errors, and extracts user intent. Real-time inference is achieved via TensorFlow Lite (TFLite), enabling low-latency, energy-efficient execution directly on mobile hardware without cloud dependency. FusionNet demonstrates robustness against ambient noise, speaker variability, and multilingual inputs, making it a practical and scalable solution for voice-driven applications. This hybrid architecture effectively combines clustering, parallel deep learning, classical ML classification, and generative AI reasoning to deliver an intelligent, high-accuracy speech recognition system tailored for real-world deployment.
Related Topics
Concepts
Computer science
Artificial intelligence
Speech recognition
Deep learning
Scalability
Robustness (evolution)
Preprocessor
Inference
Convolutional neural network
Pipeline (software)
Sphinx
Cluster analysis
Generative model
Pattern recognition (psychology)
Mobile device
Hidden Markov model
Artificial neural network
Mixture model
Autoencoder
Feature extraction
Feature (linguistics)
Deep belief network
Feature learning
Cloud computing
Generative grammar
Speech processing
Spectrogram
Android (operating system)
Hybrid system
Spectral clustering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.17862065
- OA Status
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https://openalex.org/W7111273844Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17862065Digital Object Identifier
- Title
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FusionNet: A parallel deep learning model for speech recognition with feature clusteringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-12-31Full publication date if available
- Authors
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Ramteke, Revati Harichandra, Rathod, Seema BList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17862065Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.5281/zenodo.17862065Direct OA link when available
- Concepts
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| abstract_inverted_index.Audio | 37 |
| abstract_inverted_index.MFCC, | 45 |
| abstract_inverted_index.These | 62 |
| abstract_inverted_index.cloud | 153 |
| abstract_inverted_index.fused | 94 |
| abstract_inverted_index.group | 57 |
| abstract_inverted_index.input | 38 |
| abstract_inverted_index.model | 124 |
| abstract_inverted_index.noisy | 108 |
| abstract_inverted_index.using | 54, 99 |
| abstract_inverted_index.where | 44 |
| abstract_inverted_index.Forest | 102 |
| abstract_inverted_index.Memory | 86 |
| abstract_inverted_index.Neural | 74 |
| abstract_inverted_index.Random | 101 |
| abstract_inverted_index.engine | 118 |
| abstract_inverted_index.hybrid | 4, 177 |
| abstract_inverted_index.learns | 78 |
| abstract_inverted_index.making | 166 |
| abstract_inverted_index.mobile | 150 |
| abstract_inverted_index.models | 90 |
| abstract_inverted_index.noise, | 160 |
| abstract_inverted_index.speech | 10, 60, 197 |
| abstract_inverted_index.system | 17, 199 |
| abstract_inverted_index.Android | 22 |
| abstract_inverted_index.K-Means | 55 |
| abstract_inverted_index.Network | 75 |
| abstract_inverted_index.against | 158 |
| abstract_inverted_index.ambient | 159 |
| abstract_inverted_index.deliver | 193 |
| abstract_inverted_index.enhance | 113 |
| abstract_inverted_index.errors, | 131 |
| abstract_inverted_index.inputs, | 165 |
| abstract_inverted_index.intent. | 135 |
| abstract_inverted_index.network | 88 |
| abstract_inverted_index.refines | 125 |
| abstract_inverted_index.secure, | 33 |
| abstract_inverted_index.similar | 59 |
| abstract_inverted_index.speaker | 161 |
| abstract_inverted_index.support | 32 |
| abstract_inverted_index.through | 68 |
| abstract_inverted_index.without | 152 |
| abstract_inverted_index.(BiLSTM) | 87 |
| abstract_inverted_index.Database | 30 |
| abstract_inverted_index.Firebase | 28 |
| abstract_inverted_index.Realtime | 29 |
| abstract_inverted_index.achieved | 139 |
| abstract_inverted_index.clarity, | 115 |
| abstract_inverted_index.combines | 180 |
| abstract_inverted_index.corrects | 129 |
| abstract_inverted_index.directly | 148 |
| abstract_inverted_index.enabling | 144 |
| abstract_inverted_index.extracts | 133 |
| abstract_inverted_index.features | 49 |
| abstract_inverted_index.hardware | 151 |
| abstract_inverted_index.parallel | 182 |
| abstract_inverted_index.pipeline | 43 |
| abstract_inverted_index.scalable | 171 |
| abstract_inverted_index.semantic | 114 |
| abstract_inverted_index.solution | 172 |
| abstract_inverted_index.spectral | 79 |
| abstract_inverted_index.tailored | 200 |
| abstract_inverted_index.temporal | 48, 91 |
| abstract_inverted_index.(TFLite), | 143 |
| abstract_inverted_index.FusionNet | 0, 155 |
| abstract_inverted_index.Real-time | 136 |
| abstract_inverted_index.classical | 185 |
| abstract_inverted_index.clustered | 53, 63 |
| abstract_inverted_index.execution | 147 |
| abstract_inverted_index.extracted | 51 |
| abstract_inverted_index.framework | 6 |
| abstract_inverted_index.improving | 104 |
| abstract_inverted_index.inference | 137 |
| abstract_inverted_index.learning, | 184 |
| abstract_inverted_index.on-device | 13 |
| abstract_inverted_index.parallel, | 3 |
| abstract_inverted_index.practical | 169 |
| abstract_inverted_index.processed | 67 |
| abstract_inverted_index.reasoning | 191 |
| abstract_inverted_index.segments. | 61 |
| abstract_inverted_index.spectral, | 46 |
| abstract_inverted_index.stability | 106 |
| abstract_inverted_index.supported | 119 |
| abstract_inverted_index.undergoes | 39 |
| abstract_inverted_index.(Java/XML) | 24 |
| abstract_inverted_index.Short-Term | 85 |
| abstract_inverted_index.TensorFlow | 141 |
| abstract_inverted_index.classified | 98 |
| abstract_inverted_index.contextual | 130 |
| abstract_inverted_index.embeddings | 95 |
| abstract_inverted_index.engineered | 7 |
| abstract_inverted_index.generative | 122, 189 |
| abstract_inverted_index.integrated | 26 |
| abstract_inverted_index.prediction | 105 |
| abstract_inverted_index.real-world | 202 |
| abstract_inverted_index.robustness | 157 |
| abstract_inverted_index.signatures | 80 |
| abstract_inverted_index.application | 23 |
| abstract_inverted_index.classifier, | 103 |
| abstract_inverted_index.clustering, | 181 |
| abstract_inverted_index.conditions. | 111 |
| abstract_inverted_index.dependency. | 154 |
| abstract_inverted_index.deployment. | 203 |
| abstract_inverted_index.dual-branch | 70 |
| abstract_inverted_index.effectively | 179 |
| abstract_inverted_index.implemented | 19 |
| abstract_inverted_index.management. | 36 |
| abstract_inverted_index.multi-stage | 41 |
| abstract_inverted_index.processing. | 15 |
| abstract_inverted_index.recognition | 11, 198 |
| abstract_inverted_index.acoustically | 58 |
| abstract_inverted_index.architecture | 178 |
| abstract_inverted_index.demonstrates | 156 |
| abstract_inverted_index.intelligent, | 195 |
| abstract_inverted_index.low-latency, | 145 |
| abstract_inverted_index.multilingual | 164 |
| abstract_inverted_index.user-centric | 34 |
| abstract_inverted_index.variability, | 162 |
| abstract_inverted_index.voice-driven | 174 |
| abstract_inverted_index.Bidirectional | 83 |
| abstract_inverted_index.Convolutional | 73 |
| abstract_inverted_index.applications. | 175 |
| abstract_inverted_index.architecture: | 71 |
| abstract_inverted_index.deep-learning | 5 |
| abstract_inverted_index.dependencies. | 92 |
| abstract_inverted_index.high-accuracy | 196 |
| abstract_inverted_index.preprocessing | 42 |
| abstract_inverted_index.simultaneously | 66 |
| abstract_inverted_index.speech-to-text | 14 |
| abstract_inverted_index.accent-variable | 110 |
| abstract_inverted_index.classification, | 187 |
| abstract_inverted_index.next-generation | 9 |
| abstract_inverted_index.representations | 64 |
| abstract_inverted_index.transcriptions, | 128 |
| abstract_inverted_index.energy-efficient | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.91372408 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |