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Long-term follow-up of auditory performance and Speech perception and effects of age on cochlear implantation in children with pre-lingual deafness

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Chinese Medical Journal 2019年第16期132卷 1925-1934页

作者： Jing Lyu Ying Kong Tian-Qiu Xu Rui-Juan Dong Bei-Er Qi Shuo Wang Yong-Xin Li Hai-Hong Liu Xue-Qing Chen Department of Otolaryngology Head and Neck SurgeryBeijing Tongren HospitalCapital Medical UniversityBeijing Institute of OtolaryngologyBeijing 100005China Beijing Key Laboratory for Pediatric Diseases of Otolaryngology Head and Neck SurgeryDepartment of OtolaryngologyHead and Neck SurgeryBeijing Children's HospitalCapital Medical UniversityNational Center for Children's HealthBeijing 100045China.

Background: The development of auditory and Speech perception ability of children with hearing loss is affected by many factors after they undergo cochlear implantation (CI). Age at CI (CI age) appears to play an important role among these factors. This study aimed to evaluate the development of auditory and Speech perception ability and explore the impact of CI age on children with pre-lingual deafness present before 3 years of age. Methods: Two hundred and seventy-eight children with pre-lingual deafness (176 boys and 102 girls) were included in this study, and the CI age ranged from 6 to 36 months (mean age, 19 months). Categorical auditory performance (CAP) was assessed to evaluate auditory ability, and the Speech intelligibility rating was used to evaluate Speech intelligibility. The evaluations were performed before CI and 1, 3, 6, 12, 18, 24, 36, 48, and 60 months after CI. Results: The auditory ability of the pre-lingually hearing-impaired children showed the fastest development within 6 months after CI (k = 0.524, t = 30.992, P Speech intelligibility showed the fastest improvement between the 12th and 24th months after CI (k = 0.138, t = 5.365, P Speech abilities starting at 2 years after CI (P > 0.05). The optimal cutoff age for CI was 15 months. Conclusions: Within 5 years after CI, the auditory and Speech ability of young hearing-impaired children continuously improved, although Speech development lagged behind that of hearing. An earlier CI age is recommended;the optimal cutoff age for CI is at 15 months.

关键词： Categorical auditory performance Speech intelligibility rating Cochlear implantation Children Pre-lingual deafness Age at cochlear implantation

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Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising

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Chinese Journal of Electronics 2020年第5期29卷 926-936页

作者： WANG Tingting GUO Haiyan LYU Bin YANG Zhen Broadband Wireless Communication and Sensor Network Technology Key Lab Nanjing University of Posts and Telecommunications

The paper investigates the hidden relationships among Speech samples by applying graph tools. Specifically, we first estimate an applicable graph topology for unstructured Speech signals, which can map Speech signals into the vertex domain successfully and construct as Speech graph signals(SGSs). On the basis, we define a new graph Fourier transform for SGSs, which can investigate its related graph Fourier analysis. Moreover,we propose a new Graph structure spectral subtraction(GSSS) method for Speech enhancement under different noisy environments. Simulation results show that the performance of the GSSS method can be significantly improved than the classical Basic spectral subtraction(BSS) method in terms of the average Segmental signal-tonoise ratio(SSNR), Perceptual evaluation of Speech quality(PESQ) and the computational complexity.

关键词： Graph signal processing Speech Graph Fourier transform Graph topology Denoising

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Sichuan dialect Speech recognition with deep LSTM network

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Frontiers of Computer Science 2020年第2期14卷 378-387页

作者： Wangyang YING Lei ZHANG Hongli DENG Machine Intelligence Laboratory College of Computer ScienceSichuan UniversityChengdu 610065China Education and Information Technology Center China West Normal UniversityNanchong 637002China

In Speech recognition research,because of the variety of languages,corresponding Speech recognition systems need to be constructed for different languages.Especially in a dialect Speech recognition system,there are many special words and oral language features.In addition,dialect Speech data is very scarce.Therefore,constructing a dialect Speech recognition system is difficult.This paper constructs a Speech recognition system for Sichuan dialect by combining a hidden Markov model(HMM)and a deep long short-term memory(LSTM)network.Using the HMM-LSTM architecture,we created a Sichuan dialect dataset and implemented a Speech recognition system for this dataset.Compared with the deep neural network(DNN),the LSTM network can overcome the problem that the DNN only captures the context of a fixed number of information items.Moreover,to identify polyphone and special pronunciation vocabularies in Sichuan dialect accurately,we collect all the characters in the dataset and their common phoneme sequences to form a lexicon.Finally,this system yields a 11.34%character error rate on the Sichuan dialect evaluation dataset.As far as we know,it is the best performance for this corpus at present.

关键词： Speech recognition Sichuan dialect HMMDNN HMM-LSTM Sichuan dialect lexicon

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Assessment of outcomes of hearing and Speech rehabilitation in children with cochlear implantation

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Journal of Otology 2019年第2期14卷 57-62页

作者： Shaofeng Liu Fang Wang Peipei Chen Na Zuo Cheng Wu Jun Ma Jingjiang Huang Chuanxi Wang Department of Otolaryngology-Head and Neck Surgery Yijisan Hospital of Wanan Medical CollegeWuhuAnhui241001China Department of Otolaryngology Shaoxing People's HospitalShaoxingZhejiang312000China

Objectives:This study aimed to assess the effect of hearing and Speech rehabilitation in patients with Nurotron■cochlear implants.Design:Ninety-eight paediatric patients with bilateral severe-to-profound sensorineural deafness who received cochlear implantation were divided into three groups according to age:group A(≤3 years),group B(4-7 years),and group C(8-16 years).All patients were followed up for one year for hearing and Speech performance after the surgery.The comprehensive Auditory Perception Assessment,MAIS,CAP and SIR hearing and Speech assessments and rating materials were used for assessment before the surgery and at 3,6,and 12 months after implant activation.Results:The scores of patients in the open-set Speech assessment,Chinese Auditory Perception Assessment,MAIS,CAP and SIR significantly improved after cochlear implantation in all age groups.The younger the age at implantation,the better the results.Moreover,the hearing and Speech performance of cochlear implant recipients gradually improved with the extension of rehabilitation time.Conclusions:Nurotron■Venus^TM cochlear implantation can improve the hearing and Speech performance of patients with bilateral severe-to-profound sensorineural deafness.

关键词： Hearing loss Sensorineural Cochlear implant Rehabilitation Speech perception Assessment

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Influence of Collective Esophageal Speech Training on Self-efficacy in Chinese Laryngectomees:A Pretest-posttest Group Study

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Current Medical Science 2019年第5期39卷 810-815页

作者： Qing CHEN Jing LUO Jun-ping LI Dan-ni JIAN Yong YUCHI Hong-xia RUAN Xiao-li HUANG Miao WANG Department of Otolaryngology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China

Total laryngectomy affects the speaking functions of many patients.Speech deprivation has great impacts on the quality of life of patients,especially on self-efficacy.Learning esophageal Speech represents a way to help laryngectomees speak again.The purpose of this study was to determine the influence of collective esophageal Speech training on self-efficacy of laryngectomees.In this study,28 patients and 30 family members were included.The participants received information about training via telephone or a WeChat group.Collective esophageal Speech training was used to educate laryngectomees on esophageal Speech.Before and after collective esophageal Speech training,all participants completed the General Self-Efficacy Scale(GSES)to assess their perceptions on self-efficacy.Through the training,laryngectomees recovered their Speech.After the training,the self-efficacy scores of laryngectomees were higher than those before the training,with significant differences noted(PSpeech training is not only convenient and economical,but also improves self-efficacy and confidence of laryngectomees.Greater self-efficacy is helpful for laryngectomees to master esophageal Speech and improve their quality of life.In addition,more attention should be focused on improving the self-efficacy of family members and making them give full play to their talent and potential on laryngectomees'voice rehabilitation.

关键词： laryngectomees esophageal Speech collective training self-efficacy quality of life

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Steganalysis of Low Embedding Rate CNV-QIM in Speech

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Computer Modeling in Engineering & Sciences 2021年第8期128卷 623-637页

作者： Wanxia Yang Miaoqi Li Beibei Zhou Yan Liu Kenan Liu Zhiyu Hu Mechanical and Electrical Engineering College Gansu Agricultural UniversityLanzhou730070China

To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM(complementary neighbor vertices-quantization index modulation)steganography in low bit-rate Speech codec,the code-word correlation model based on a BiLSTM(bi-directional long short-term memory)neural network is built to obtain the correlation features of the LPC codewords in Speech codec in this paper.Then,softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams.The experimental results show that for Speech steganography of short samples with low embedding rate,the BiLSTM method in this paper has a superior detection accuracy than state-of-the-art methods of the RNN-SM(recurrent neural network-steganalysis model)and SS-QCCN(simplest strong quantization codeword correlation network).At an embedding rate of 20%and a duration of 3 s,the detection accuracy of BiLSTM method reaches 75.7%,which is higher than that of RNNSM by 11.7%.Furthermore,the average testing time of samples(100%embedding)is 0.3 s,which shows that the method can realize real-time steganography detection of VoIP streams.

关键词： CNV-QIM steganography BiLSTM steganalysis VoIP Speech

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Binary neural networks for Speech recognition

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Frontiers of Information Technology & Electronic Engineering 2019年第5期20卷 701-715页

作者： Yan-min QIAN Xu XIANG Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai 200240 China SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China

Recently, deep neural networks(DNNs) significantly outperform Gaussian mixture models in acoustic modeling for Speech recognition. However, the substantial increase in computational load during the inference stage makes deep models difficult to directly deploy on low-power embedded devices. To alleviate this issue,structure sparseness and low precision fixed-point quantization have been applied widely. In this work, binary neural networks for Speech recognition are developed to reduce the computational cost during the inference stage.A fast implementation of binary matrix multiplication is introduced. On modern central processing unit(CPU)and graphics processing unit(GPU) architectures, a 5–7 times speedup compared with full precision floatingpoint matrix multiplication can be achieved in real applications. Several kinds of binary neural networks and related model optimization algorithms are developed for large vocabulary continuous Speech recognition acoustic modeling. In addition, to improve the accuracy of binary models, knowledge distillation from the normal full precision floating-point model to the compressed binary model is explored. Experiments on the standard Switchboard Speech recognition task show that the proposed binary neural networks can deliver 3–4 times speedup over the normal full precision deep models. With the knowledge distillation from the normal floating-point models, the binary DNNs or binary convolutional neural networks(CNNs) can restrict the word error rate(WER) degradation to within 15.0%,compared to the normal full precision floating-point DNNs or CNNs, respectively. Particularly for the binary CNN with binarization only on the convolutional layers, the WER degradation is very small and is almost negligible with the proposed approach.

关键词： Speech recognition Binary neural net works Binary matrix multiplication Knowledge distillation Population count

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Gain adjustment at tinnitus pitch to manage both tinnitus and Speech perception in noise

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Journal of Otology 2019年第4期14卷 141-148页

作者： Hemanth Narayan Shetty Jijo Mathai Pottackal Department of Audiology All India Institute of Speech and HearingMysuru570006India Department of Speech and Hearing JSS Institute of Speech and HearingDharwad580007India

To investigate how much gain variation is required from prescription to effect tinnitus percept,and if this revised prescription affects Speech recognition.Twenty participants who experienced catastrophic tinnitus even after fitted with hearing aid were included.Participants were grouped based on their tinnitus pitch and the prescriptive formula used to fit hearing aid.They were evaluated for handicap from tinnitus using Tinnitus Handicap Inventory(THI).Hearing aid was programmed using either NAL-NL2 or DSL(I/o)v5 prescriptive formula and gain at tinnitus pitch was adjusted till the tinnitus get suppressed.SNR 50 was determined soon after fitted with hearing aid and 30 days of hearing aid use.Further,THI and international outcome inventory for hearing aid(IOI-HA)were determined after 30 days of hearing aid use.A significant higher gain adjustment was needed at tinnitus pitch to reduce tinnitus precept using NAL-NL2 than DSL(I/o)v5 prescriptive formula.Further,SNR 50 was not affected by either tinnitus pitch or revised prescription formulas.However,SNR 50 improved after 30 days of hearing aid use.A 76% of the participants’experienced habituation to perception after 30 days of hearing aid use,10% had slight,10% had mild,and 4% had a moderate degree of tinnitus on THI.On IOA-HA,96%(N=19)of participants have reported satisfactory,and 4%(N=1)reported moderate benefit from hearing aid.Irrespective of prescriptive formula adjusting gain at tinnitus pitch is an efficient method to reduce tinnitus symptoms and improve Speech perception.

关键词： Amplification Hearing loss Tinnitus Speech perception Hearing aid

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A non-intrusive Speech quality evaluation algorithm combining auxiliary target learning and convolutional recurrent network

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Chinese Journal of Acoustics 2023年第2期42卷 235-250页

作者： TANG Guichen LIANG Ruiyu KONG Fanliu XIE Yue JU Mengjie School of Information and Communication Engineering Nanjing Institute of TechnologyNanjing 211167 School of Information Science and Engineering Southeast UniversityNanjing 210096

The objective evaluation of Speech quality can replace expensive manual scoring,but current objective indicators usually need pure reference Speech,which is difficult to obtain in many practical acoustic systems.A non-intrusive Speech quality evaluation algorithm combining auxiliary target learning and a convolutional recurrent network(CRN)is proposed.Bark frequency cepstral coefficients(BFCCs),which are based on human-like auditory filters,are used as the input of the CRN network to effectively reduce the network complexity.Firstly,frame-level features are extracted by a convolutional neural network(CNN)from BFCCs.Then,long-term time dependence and sequence features are modeled by the bidirectional long shortterm memory(BiLSTM)networks in frame-level features.Finally,a self-attention mechanism is introduced into the CRN,thereby adaptively extracting useful information from frame-level features,which is then integrated into the characteristics of the sentence level and mapped into the final objective score.In addition,a multi-task training strategy is adopted,and voice activity detection(VAD)is introduced as an auxiliary learning target to improve the performance of the algorithm.Experiments in public databases show that compared with other non-intrusive algorithms,the proposed algorithm has a better correlation with the mean opinion score(MOS).Moreover,it has a small parameter size and good generalization ability for the distorted Speech database with MOS released by ITU-T P.808,which is close to the accuracy of the perceptual evaluation of Speech quality(PESQ).

关键词： network Speech algorithm

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Monaural Speech enhancement using U-net fused with multi-head self-attention

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Chinese Journal of Acoustics 2023年第1期42卷 98-118页

作者： FAN Junyi YANG Jibin ZHANG Xiongwei ZHENG Changyan Graduate School Army Engineering UniversityNanjing 210007 College of Command and Control Engineering Army Engineering UniversityNanjing 210007 Department of Test Control High-tech InstituteQingzhou 262500

Under low signal-to-noise ratio(SNR)and burst noise conditions,the Speech enhancement effect of existing deep learning network models is not satisfactory.In contrast,humans can exploit the long-term correlation of Speech to form an integrated perception of different Speech signals.Thus,describing the long-term dependencies of Speech can help improve the enhancement performance under low SNR and burst noise conditions.Inspired by this feature,a time domain end-to-end monaural Speech enhancement model TU-net that fuses the multi-head self-attention mechanism and U-net deep network is proposed.The TU-net model adopts the codec layer structure of U-net to achieve multi-scale feature fusion.It introduces the dual-path Transformer module using the multi-head self-attention mechanism to calculate the Speech mask and better model long-term correlation.The TU-net model is trained with a weighted sum loss function in the time,time-frequency,and perceptual domains.Simulation experiments are carried out and the results show that with maintaining relatively fewer network model parameters,TU-net outperforms other similar monaural enhancement network models in several evaluation metrics such as perceptual evaluation of Speech quality(PESQ),short-time objective intelligibility(STOI)and SNR gain under low SNR and burst noise conditions.

关键词： network Speech noise

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