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RSG-Conformer:ReLU-Based Sparse and Grouped Conformer for Audio-Visual Speech Recognition 认领 引用
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作者 Yewei Xiao Xin Du Wei Zeng 《Computers, Materials & Continua》 SCIE EI 2026年第3期1325-1348,共24页
Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.... Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks. 展开更多
关键词 Audio-visual speech recognition conformer CTC sparse attention
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Exploring Generation of Pronunciation Lexicon for Low-Resource Language Automatic Speech Recognition Based on Generic Phone Recognizer 认领 引用
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作者 LI Jinpeng CHEN Xie ZHANG Weiqiang 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期265-272,共8页
The lexicon is an essential component in the hybrid automatic speech recognition(ASR)system.However,a high-quality lexicon requires significant efforts from the linguistic experts and is difficult to obtain,especially... The lexicon is an essential component in the hybrid automatic speech recognition(ASR)system.However,a high-quality lexicon requires significant efforts from the linguistic experts and is difficult to obtain,especially for low-resource languages.This paper addresses the problem of using a well-trained universal phone recognizer,obtained through the training of multilingual speech data and pronunciation lexicons,to generate pronunciation lexicons for low-resource languages driven by speech data.We propose a simple pipeline that utilizes this approach to generate pronunciation lexicons and apply them into ASR systems.The steps to generate the lexicon are simple and generic:applying the International Phonetic Alphabet(IPA)phone recognizer on the speech,then aligning it with the reference word sequence,followed by filtering to obtain a series of AUTO-subwords,using them to generate the AUTO-subword lexicon and the AUTO-IPA lexicon.We used the pronunciation lexicon generated for the hybrid system and for fine-tuning the pre-trained model.According to the experiment results,we are able to construct the lexicon without resourcing to linguistic experts.Furthermore,the generated lexicon is able to outperform grapheme-based lexicon and is comparable to expert lexicon. 展开更多
关键词 International Phonetic Alphabet(IPA) lexicon learning phone recognition low-resource speech recognition
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NestLipGNN:A Hierarchical Graph Neural Network Framework with Nested Multi-Granularity Learning for Robust Visual Speech Recognition 认领 引用
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作者 Vinh Truong Hoang Nghia Dinh +5 位作者 Luu Quang Phuong Kiet Tran-Trung Ha Duong Thi Hong Bay Nguyen Van Hau Nguyen Trung Thien Ho Huong 《Computers, Materials & Continua》 SCIE EI 2026年第7期1287-1310,共24页
Visual speech recognition(VSR)aims to infer spoken content from visual observations of articulatory movements.Despite significant progress,it remains a challenging task in computer vision and speech processing.Its dif... Visual speech recognition(VSR)aims to infer spoken content from visual observations of articulatory movements.Despite significant progress,it remains a challenging task in computer vision and speech processing.Its difficulty arises from pronounced speaker-to-speaker variability,the presence of homophenes(phonemes that are visually indistinguishable),changes in illumination,and the intrinsically high-dimensional nature of spatiotemporal lip dynamics.In this work,we propose NestLipGNN,a graph-based framework that integrates Graph Neural Networks(GNNs)with a nested multi-granularity learning strategy for visual speech recognition.We construct dynamic lip graphs from facial landmarks to model both spatial relationships between lip regions and their temporal motion during speech articulation.The proposed nested learning architecture supports hierarchical feature extraction across several levels of linguistic abstraction,spanning phoneme-level articulatory units,viseme-level visual speech categories,and word-level semantic representations.We further introduce a Temporal Graph Attention mechanism(T-GAT)that adaptively reweights the importance of distinct lip regions over time.We also introduce a graph-based contrastive learning objective to improve the discrimination of visually similar speech patterns,directly confronting the challenge of homophene resolution.Experiments on the LRW,LRS2,LRS3,and GRID datasets show that NestLipGNN improves recognition accuracy compared with existing methods,obtaining 92.3%word-level accuracy on LRW and delivering a 2.1%absolute performance gain over prior methods.Comprehensive ablation analyses confirm the contribution of each architectural component. 展开更多
关键词 Visual speech recognition graph neural networks nested optimization hierarchical representation learning spatiotemporal modeling contrastive learning lip reading
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Wav2vec-AD:Acoustic Unit Discovery Module-Integrated,Self-Supervised Contrastive Pre-training Approach for Speech Recognition 认领 引用
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作者 NURMEMET Yolwas SUN Lixu +2 位作者 LI Xin LIU Qichao WANG Zhixiang 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期289-297,共9页
An effective speech recognition model necessitates an ample supply of labeled data for supervised training.However,this proposition poses a monumental challenge for low-resource languages in terms of constructing a sp... An effective speech recognition model necessitates an ample supply of labeled data for supervised training.However,this proposition poses a monumental challenge for low-resource languages in terms of constructing a speech recognition system with high precision.In this paper,we propose a novel pre-training strategy for contrastive learning by fusing the acoustic unit discovery module with Wav2vec 2.0,herein referred to asWav2vec-AD.This strategy,for the first time in speech contrastive learning,enables controlled negative sample selection via the acoustic unit discovery module,thereby augmenting the model’s representational learning capability.Furthermore,we conduct a thorough analysis regarding the selection of negative samples in different situations to enhance the speech representation learned by the model,optimizing its efficacy in downstream tasks.In the low-resource case,compared to the baseline Wav2vec 2.0,Wav2vec-AD achieves absolute word error rate(WER)improvements of 1.55%and 1.46%respectively on the development-clean and test-clean subsets of LibriSpeech.Moreover,absolute WER improvements of 0.63%and 4.21%were realized in Arabic and Turkish language datasets,respectively. 展开更多
关键词 self-supervised learning automatic speech recognition contrastive learning low-resource
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EC-BERT:A BERT Language Model with Error Correction for Mandarin Chinese Speech Recognition 认领 引用
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作者 XIAO Sujie HAO Ruipeng +2 位作者 CHENG Gaofeng XU Xiaoyan LI Ta 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期282-288,共7页
The attention-based encoder-decoder end-to-end model has achieved promising performance in automatic speech recognition(ASR).However,in practical applications,substitution errors commonly occur in ASR systems,particul... The attention-based encoder-decoder end-to-end model has achieved promising performance in automatic speech recognition(ASR).However,in practical applications,substitution errors commonly occur in ASR systems,particularly for characters with the same or similar pronunciation.According to statistics,homophones cause at least 50%character errors.Therefore,our study focuses on addressing the issue of substitution errors with the same or similar pronunciation.In this study,we propose a BERT language model with error correction(EC-BERT)for the ASR system.We design a two-stage training schedule involving pre-training with a large amount of pseudo-paired data followed by fine-tuning with a small real-paired data to mitigate the inconsistency of the original pre-trained BERT model with our task.Unlike other error correction models,we do not need an error detection network or mask mechanism but directly use the BERT model to learn and correct the error locations.The experimental results show that our proposed method is effective and achieves a relative reduction of 19.2%in character error rate compared with the connectionist temporal classification(CTC)greedy search result and 12.8%compared with the CTC-WFST result on the AISHELL-1 test set.We also prove that our proposed EC-BERT model can achieve comparable results to other error correction models with a shorter runtime and can easily be integrated into the practical ASR system. 展开更多
关键词 automatic speech recognition(ASR) end-to-end BERT error correction
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Multi-Frame Cross-Channel Attention and Speaker Diarization Based Speaker-Attributed Automatic Speech Recognition System for Multi-Channel Multi-Party Meeting Transcription 认领 引用
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作者 XU Luzhen YAN Haoyin +5 位作者 HE Maokui GUO Zixian ZHOU Yeping LIU Peiqi ZHANG Jie DAI Lirong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期298-304,共7页
This paper describes a speaker-attributed automatic speech recognition(SA-ASR)system submitted to the multi-channel multi-party meeting transcription challenge,which aims to address the“who spoke what”problem.We ali... This paper describes a speaker-attributed automatic speech recognition(SA-ASR)system submitted to the multi-channel multi-party meeting transcription challenge,which aims to address the“who spoke what”problem.We align the serialized output training-based multi-speaker ASR hypotheses and speaker diarization(SD)results to obtain speaker-attributed transcriptions.We use a pre-trained multi-frame cross-channel attention(MFCCA)model as the ASR module.We build a cascade system which includes a pre-trained speaker overlapaware neural diarization and target-speaker voice activity detection model as the SD module.Decoding and alignment strategies are further used to improve the SA-ASR performance.Our proposed system outperforms the baseline with a relative improvement of 40.3%in terms of concatenated minimum-permutation character error rate on the AliMeeting dataset,which ranks top-3 on the fixed sub-track. 展开更多
关键词 multi-channel multi-party meeting transcription speaker-attributed automatic speech recognition(SA-ASR) serialized output training speaker diarization concatenated minimum-permutation character error rate
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TENG-Based Self-Powered Silent Speech Recognition Interface:from Assistive Communication to Immersive AR/VR Interaction 认领 引用
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作者 Shuai Lin Yanmin Guo +4 位作者 Xiangyao Zeng Xiongtu Zhou Yongai Zhang Chengda Li Chaoxing Wu 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第5期31-44,共14页
Lip language provides a silent,intuitive,and efficient mode of communication,offering a promising solution for individuals with speech impairments.Its articulation relies on complex movements of the jaw and the muscle... Lip language provides a silent,intuitive,and efficient mode of communication,offering a promising solution for individuals with speech impairments.Its articulation relies on complex movements of the jaw and the muscles surrounding it.However,the accurate and real-time acquisition and decoding of these movements into reliable silent speech signals remains a significant challenge.In this work,we propose a real-time silent speech recognition system,which integrates a triboelectric nanogenerator-based flexible pressure sensor(FPS)with a deep learning framework.The FPS employs a porous pyramid-structured silicone film as the negative triboelectric layer,enabling highly sensitive pressure detection in the low-force regime(1 V N-1 for 0-10 N and 4.6 V N-1 for 10-24 N).This allows it to precisely capture jaw movements during speech and convert them into electrical signals.To decode the signals,we proposed a convolutional neural networklong short-term memory(CNN-LSTM)hybrid network,combining CNN and LSTM model to extract both local spatial features and temporal dynamics.The model achieved 95.83%classification accuracy in 30 categories of daily words.Furthermore,the decoded silent speech signals can be directly translated into executable commands for contactless and precise control of the smartphone.The system can also be connected to AR glasses,offering a novel human-machine interaction approach with promising potential in AR/VR applications. 展开更多
关键词 Flexible pressure sensor Silent speech recognition Triboelectric nanogenerator Deep learning AR/VR interaction
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Ti3C2Tx Composite Aerogels Enable Pressure Sensors for Dialect Speech Recognition Assisted by Deep Learning 认领 引用 被引量：5
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作者 Yanan Xiao He Li +8 位作者 Tianyi Gu Xiaoteng Jia Shixiang Sun Yong Liu Bin Wang He Tian Peng Sun Fangmeng Liu Geyu Lu 《Nano-Micro Letters》 SCIE EI CAS CSCD 2025年第5期1-15,共15页
Wearable pressure sensors capable of adhering comfortably to the skin hold great promise in sound detection.However,current intelligent speech assistants based on pressure sensors can only recognize standard languages... Wearable pressure sensors capable of adhering comfortably to the skin hold great promise in sound detection.However,current intelligent speech assistants based on pressure sensors can only recognize standard languages,which hampers effective communication for non-standard language people.Here,we prepare an ultralight Ti3C2TxMXene/chitosan/polyvinylidene difluoride composite aerogel with a detection range of 6.25 Pa-1200 k Pa,rapid responseecovery time,and low hysteresis(13.69%).The wearable aerogel pressure sensor can detect speech information through the throat muscle vibrations without any interference,allowing for accurate recognition of six dialects(96.2%accuracy)and seven different words(96.6%accuracy)with the assistance of convolutional neural networks.This work represents a significant step forward in silent speech recognition for human–machine interaction and physiological signal monitoring. 展开更多
关键词 Pressure sensor Wearable sensor Ti3C2Tx composite aerogel Dialect speech recognition
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The Effect of Binaural Beamforming Technology on Mandarin Speech Recognition in Babble Noise for Bimodal Hearing CI users 认领 引用
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作者 Aiting Chen Mengdi Hong +17 位作者 Jianan Li Qian Wang Nan Li Lumeng Han Qian Wu Haihong Liu Yidi Liu Yue Long Fangxia Hu Jianfen Luo Lei Xu Zhaomin Fan Peng Lin Wei Wang Yue Wang Yu Chen Zhaohui Hou Fei Ji 《Journal of Otology》 CSCD 2025年第3期157-161,共5页
PurposeThe purpose of the study was to investigate the effect of bimodal beamforming on speech recognition and comfort for cochlear implant (CI) users with the bimodal hearing solution made up by linking a hearing aid... PurposeThe purpose of the study was to investigate the effect of bimodal beamforming on speech recognition and comfort for cochlear implant (CI) users with the bimodal hearing solution made up by linking a hearing aid to the CI sound processor.Methods19 subjects participated in this study. Speech tests were conducted in quiet and in noisy environments, with the target speech presented from 0° and the noise signal from 45°. Speech recognition thresholds (SRTs) were compared among the previously used bimodal hearing configuration (baseline, any CI sound processor plus any hearing aid), the Naída Bimodal Hearing Solution with omnidirectional microphone, and with directional microphone (so called StereoZoom) switched on. In addition, the study participants provided subjective feedback on their hearing impressions.ResultsThe SRT results showed no significant difference among the three hearing conditions in the quiet environment. No significant improvement was reported when using Naída bimodal system with omnidirectional microphone in noise compared to the baseline (p=0.27). When applying StereoZoom, SRT in noise showed significant improvements compared to omnidirectional settings (p<0.05). Subjective feedback showed that 13 participants were satisfied with Naída Bimodal Hearing Solution, and wanted to continue using it after the trial.ConclusionThe Naída Bimodal Hearing Solution with the same pre-processing algorithm can provide satisfying hearing performance. Beamforming technology can further improve speech perception in noisy environments. 展开更多
关键词 bimodal Cochlear Implant speech recognition beamforming directional microphone
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Speech Recognition via CTC-CNN Model 认领 引用
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作者 Wen-Tsai Sung Hao-WeiKang Sung-Jung Hsiao 《Computers, Materials & Continua》 SCIE EI 2023年第9期3833-3858,共26页
In the speech recognition system,the acoustic model is an important underlying model,and its accuracy directly affects the performance of the entire system.This paper introduces the construction and training process o... In the speech recognition system,the acoustic model is an important underlying model,and its accuracy directly affects the performance of the entire system.This paper introduces the construction and training process of the acoustic model in detail and studies the Connectionist temporal classification(CTC)algorithm,which plays an important role in the end-to-end framework,established a convolutional neural network(CNN)combined with an acoustic model of Connectionist temporal classification to improve the accuracy of speech recognition.This study uses a sound sensor,ReSpeakerMic Array v2.0.1,to convert the collected speech signals into text or corresponding speech signals to improve communication and reduce noise and hardware interference.The baseline acousticmodel in this study faces challenges such as long training time,high error rate,and a certain degree of overfitting.The model is trained through continuous design and improvement of the relevant parameters of the acousticmodel,and finally the performance is selected according to the evaluation index.Excellentmodel,which reduces the error rate to about 18%,thus improving the accuracy rate.Finally,comparative verificationwas carried out from the selection of acoustic feature parameters,the selection of modeling units,and the speaker’s speech rate,which further verified the excellent performance of the CTCCNN_5+BN+Residual model structure.In terms of experiments,to train and verify the CTC-CNN baseline acoustic model,this study uses THCHS-30 and ST-CMDS speech data sets as training data sets,and after 54 epochs of training,the word error rate of the acoustic model training set is 31%,the word error rate of the test set is stable at about 43%.This experiment also considers the surrounding environmental noise.Under the noise level of 80∼90 dB,the accuracy rate is 88.18%,which is the worst performance among all levels.In contrast,at 40–60 dB,the accuracy was as high as 97.33%due to less noise pollution. 展开更多
关键词 Artificial intelligence speech recognition speech to text convolutional neural network automatic speech recognition
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Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems 认领 引用 被引量：4
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作者 Sneha Basak Himanshi Agrawal +4 位作者 Shreya Jena Shilpa Gite Mrinal Bachute Biswajeet Pradhan Mazen Assiri 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第5期1053-1089,共37页
Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computa... Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution. 展开更多
关键词 Speech recognition automatic speech recognition(ASR) mel-frequency cepstral coefficients(MFCC) hidden Markov model(HMM) artificial neural network(ANN)
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Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system 认领 引用 被引量：5
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作者 黄丽霞 G.Evangelista 张雪英 《Journal of Central South University》 SCIE EI CAS 2011年第5期1595-1601,共7页
Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher acc... Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results. 展开更多
关键词 perceptual filter banks bark scale speaker independent speech recognition systems zero-crossing peak amplitude genetic algorithm
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Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications 认领 引用 被引量：3
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作者 Shuting Ge Jin Ren +3 位作者 Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang 《Computers, Materials & Continua》 SCIE EI 2024年第3期3215-3245,共31页
In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a p... In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management. 展开更多
关键词 Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture
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Audiovisual speech recognition based on a deep convolutional neural network 认领 引用 被引量：2
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作者 Shashidhar Rudregowda Sudarshan Patilkulkarni +2 位作者 Vinayakumar Ravi Gururaj H.L. Moez Krichen 《Data Science and Management》 2024年第1期25-34,共10页
Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for India... Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual recognition.Audio features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural network.Visual feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural networks.Finally,integration was performed using a deep convolutional network.The audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 epochs.The training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 epochs.After integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively. 展开更多
关键词 Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)
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Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank 认领 引用 被引量：1
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作者 Shibli Nisar Muhammad Asghar Khan +3 位作者 Fahad Algarni Abdul Wakeel M.Irfan Uddin Insaf Ullah 《Computers, Materials & Continua》 SCIE EI 2022年第2期2991-3004,共14页
One of the most commonly reported disabilities is vision loss,which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient.This procedure,however,usually requires an appointment wi... One of the most commonly reported disabilities is vision loss,which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient.This procedure,however,usually requires an appointment with an ophthalmologist,which is both time-consuming and expensive process.Other issues that can arise include a lack of appropriate equipment and trained practitioners,especially in rural areas.Centered on a cognitively motivated attribute extraction and speech recognition approach,this paper proposes a novel idea that immediately determines the eyesight deficiency.The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction.The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment.Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth,established by the expert ophthalmologist’s tests.The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%.The proposed method potentially offers a second opinion to ophthalmologists,and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage. 展开更多
关键词 Eyesight test speech recognition HMM SVM feature extraction
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Mandarin Digits Speech Recognition Using Support Vector Machines 认领 引用 被引量：3
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作者 XIE Xiang KUANG Jing-ming 《Journal of Beijing Institute of Technology》 EI CAS 2005年第1期9-12,共4页
A method of applying support vector machine(SVM)in speech recognition was proposed,and a speech recognition system for mandarin digits was built up by SVMs.In the system,vectors were linearly extracted from speech fea... A method of applying support vector machine(SVM)in speech recognition was proposed,and a speech recognition system for mandarin digits was built up by SVMs.In the system,vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM,and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier.Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits.And the kernel of radial basis function has the highest accurate rate of 99.33%,which is better than that of the baseline system based on hidden Markov models(HMM)(97.08%).And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited. 展开更多
关键词 speech recognition support vector machine(SVM) kernel function
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Novel Active Learning Method for Speech Recognition 认领 引用 被引量：1
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作者 Liu Gang Chen Wei Guo Jun 《China Communications》 SCIE 2010年第5期29-39,共11页
In speech recognition,acoustic modeling always requires tremendous transcribed samples,and the transcription becomes intensively time-consuming and costly.In order to aid this labor-intensive process,Active Learning(A... In speech recognition,acoustic modeling always requires tremendous transcribed samples,and the transcription becomes intensively time-consuming and costly.In order to aid this labor-intensive process,Active Learning(AL)is adopted for speech recognition,where only the most informative training samples are selected for manual annotation.In this paper,we propose a novel active learning method for Chinese acoustic modeling,the methods for initial training set selection based on Kullback-Leibler Divergence(KLD)and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system,respectively.Our experiments show that our proposed method can achieve satisfying performances. 展开更多
关键词 active learning acoustic model speech recognition KLD confusion network
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Fuzzy C-Means Clustering Based Phonetic Tied-Mixture HMM in Speech Recognition 认领 引用 被引量：1
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作者 徐向华 朱杰 郭强 《Journal of Shanghai Jiaotong university(Science)》 EI 2005年第1期16-20,共5页
A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-... A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02% respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy. 展开更多
关键词 speech recognition hidden Markov model (HMM) fuzzy C-means (FCM) phonetic decision tree
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Improved hidden Markov model for speech recognition and POS tagging 认领 引用 被引量：5
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作者 袁里驰 《Journal of Central South University》 SCIE EI CAS 2012年第2期511-516,共6页
In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language proc... In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system. 展开更多
关键词 hidden Markov model Markov family model speech recognition part-of-speech tagging
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Data-Driven Temporal Filtering on Teager Energy Time Trajectory for Robust Speech Recognition 认领 引用 被引量：1
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作者 赵军辉 谢湘 匡镜明 《Journal of Beijing Institute of Technology》 EI CAS 2006年第2期195-200,共6页
Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation （TEO） based feature parameter for improving the robustness of speech recognition system against noise. Three... Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation （TEO） based feature parameter for improving the robustness of speech recognition system against noise. Three kinds of data-driven temporal filters are investigated for the motivation of alleviating the harmful effects that the environmental factors have on the speech. The filters include： principle component analysis （PCA） based filters, linear discriminant analysis （LDA） based filters and minimum classification error （MCE） based filters. Detailed comparative analysis among these temporal filtering approaches applied in Teager energy domain is presented. It is shown that while all of them can improve the recognition performance of the original TEO based feature parameter in adverse environment, MCE based temporal filtering can provide the lowest error rate as SNR decreases than any other algorithms. 展开更多
关键词 robust speech recognition principle component analysis linear discriminant analysis minimum classification error
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