Rethinking multi-spatial information for transferable adversarial attacks on speaker recognition systems

Adversarial attacks have been posing significant security concerns to intelligent systems,such as speaker recognition systems(SRSs).Most attacks assume the neural networks in the systems are known beforehand,while black-box attacks are proposed without such information to meet practical situations.Existing black-box attacks improve trans-ferability by integrating multiple models or training on multiple datasets,but these methods are costly.Motivated by the optimisation strategy with spatial information on the perturbed paths and samples,we propose a Dual Spatial Momentum Iterative Fast Gradient Sign Method(DS-MI-FGSM)to improve the transferability of black-box at-tacks against SRSs.Specifically,DS-MI-FGSM only needs a single data and one model as the input;by extending to the data and model neighbouring spaces,it generates adver-sarial examples against the integrating models.To reduce the risk of overfitting,DS-MI-FGSM also introduces gradient masking to improve transferability.The authors conduct extensive experiments regarding the speaker recognition task,and the results demonstrate the effectiveness of their method,which can achieve up to 92%attack success rate on the victim model in black-box scenarios with only one known model.

An Efficient Text-Independent Speaker Identification Using Feature Fusion and Transformer Model

Automatic Speaker Identification(ASI)involves the process of distinguishing an audio stream associated with numerous speakers’utterances.Some common aspects,such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording,make the ASI task much more complicated and complex.This research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation resources.In this research,the performance of the transformer model is investigated.Seven audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted from the ELSDSR,CSTRVCTK,and Ar-DAD,datasets.The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on all datasets.For ELSDSR,CSTRVCTK,and Ar-DAD,the highest attained accuracies are 0.99,0.97,and 0.99,respectively.The experimental results reveal that the proposed technique can achieve the best performance for ASI problems.

Optical Ciphering Scheme for Cancellable Speaker Identification System

Most current security and authentication systems are based on personal biometrics.The security problem is a major issue in the field of biometric systems.This is due to the use in databases of the original biometrics.Then biometrics will forever be lost if these databases are attacked.Protecting privacy is the most important goal of cancelable biometrics.In order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases.One methodology to achieve non-invertibility is the employment of non-invertible transforms.This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system.This system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption process.The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and feasible.The encryption and cancelability effects are good and reveal good performance.Also,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

Voice Response Questionnaire System for Speaker Recognition Using Biometric Authentication Interface

The use of voice to perform biometric authentication is an importanttechnological development,because it is a non-invasive identification methodand does not require special hardware,so it is less likely to arouse user disgust.This study tries to apply the voice recognition technology to the speech-driveninteractive voice response questionnaire system aiming to upgrade the traditionalspeech system to an intelligent voice response questionnaire network so that thenew device may offer enterprises more precise data for customer relationshipmanagement(CRM).The intelligence-type voice response gadget is becominga new mobile channel at the current time,with functions of the questionnaireto be built in for the convenience of collecting information on local preferencesthat can be used for localized promotion and publicity.Authors of this study propose a framework using voice recognition and intelligent analysis models to identify target customers through voice messages gathered in the voice response questionnaire system;that is,transforming the traditional speech system to anintelligent voice complex.The speaker recognition system discussed hereemploys volume as the acoustic feature in endpoint detection as the computationload is usually low in this method.To correct two types of errors found in the endpoint detection practice because of ambient noise,this study suggests ways toimprove the situation.First,to reach high accuracy,this study follows a dynamictime warping(DTW)based method to gain speaker identification.Second,it isdevoted to avoiding any errors in endpoint detection by filtering noise from voicesignals before getting recognition and deleting any test utterances that might negatively affect the results of recognition.It is hoped that by so doing the recognitionrate is improved.According to the experimental results,the method proposed inthis research has a high recognition rate,whether it is on personal-level or industrial-level computers,and can reach the practical application standard.Therefore,the voice management system in this research can be regarded as Virtual customerservice staff to use.

Efficiency in Teaching Speaker and Listener Repertoires:Comparing Three Instructional Sequences in Autistic Children

Previous studies have investigated the efficiency in teaching listener and speaker repertoires in children diagnosed with autism spectrum disorder(ASD).Some investigations focused on listener responding by function,feature,and class(LRFFC)and intraverbal by function,feature,and class(FFC).For some children,teaching intraverbal FFC was more efficient because it resulted in a better emergence effect of a related untaught repertoire(LRFFC).For other children,teaching LRFFC along with tacting pictures was more efficient,resulting in a better emergence effect of a related untaught repertoire(intraverbal FFC).In these cases,it is not clear whether the tact increased the efficiency of LRFFC training because a comparison with a condition in which tacts were not required was not conducted.This investigation consisted of a replication with two children diagnosed with ASD.Three instructional sequences were compared:teaching LRFFC-probing intraverbal;teaching LRFFC+tacts-probing intraverbal;teaching intraverbal-probing LRFFC.For a child,all sequences were equally efficient because all related untaught repertoires emerged without errors.However,the acquisition of intraverbals during training occurred with variability.In the case of the second child,the most efficient sequence consisted of teaching intraverbals,resulting in the emergence of LRFFC without errors.In both cases of teaching LRFFC,the emergence of related intraverbals was partial and acquisition of the trained repertoires occurred with variability.The case that did not demand tact responses was slightly more efficient.Data were discussed in the sense that the best instructional sequence may vary from learner to learner.

Native-speakerism in China:An Investigation of Foreign Language Education Policy and Learners’Attitudes

With the development of globalization,the use of English is no longer restricted to native speaker(NS)but also widely spread to non-native speaker(NNS).The importance of English learning is also acknowledged by Expanding and Outer Circle,and English as a foreign language(EFL)education plays a significant role in China’s education.Admitting the fact that non-native English teachers(NNESTs)take up a large proportion of English teachers,English language teaching(ELT)is still greatly influenced by native-speakerism.This research aims to investigate language ideologies reflected in Chinese foreign language education policy(FLEP)at higher education level,and Chinese English learners’attitudes towards native-speakerism and English teachers.A mixed method of policy analysis and survey is adopted in this research.After conducting analysing two FLEPs in higher education level,it is found that linguistic instrumentalism is the prominent language ideology,although native-speakerism and standard English ideology is implicitly demonstrated.Questionnaire is used to investigate 58 Chinese English learners’attitudes,revealing that most participants do not demonstrate bias towards either NESTs or NNESTs.Instead,the strengths and weaknesses of both NEST and NNEST are identified,though participants adhere to native-speakerism in terms of English variety.Overall,English learner’s attitudes are consistent with language ideologies in FLEPs.This research may provide implications for future studies on addressing native-speakerism in Chinese FLEPs,as well as relationship of students’attitudes and language policies.

Emotional speaker recognition based on prosody transformation

A novel emotional speaker recognition system （ESRS） is proposed to compensate for emotion variability. First, the emotion recognition is adopted as a pre-processing part to classify the neutral and emotional speech. Then, the recognized emotion speech is adjusted by prosody modification. Different methods including Gaussian normalization, the Gaussian mixture model （GMM） and support vector regression （SVR） are adopted to define the mapping rules of F0s between emotional and neutral speech, and the average linear ratio is used for the duration modification. Finally, the modified emotional speech is employed for the speaker recognition. The experimental results show that the proposed ESRS can significantly improve the performance of emotional speaker recognition, and the identification rate （IR） is higher than that of the traditional recognition system. The emotional speech with F0 and duration modifications is closer to the neutral one.

Native Speaker and L2 User in Language Teaching

The target of much language teaching and learning is to make students approximate to native speakers.The only rightful speak ers of a language are its native speakers.Contrary to these contemporary views,however,this paper argues that the obligation of the lan guage teacher is to help students to use L2 effectively not to simply imitate native speaker.A successful L2 user who comes from the group of L2 learners can be a model for students.Therefore,non-native teachers with a high degree of language proficiency and good teaching skills can be ideal and qualified language teachers.

A Critique of Ali Shehadeh's Non-native Speakers'Production of Modified Comprehensible Output and Second Language Learning

This study examined the NNSs' ability of modifying their interlanguage utterances in modified comprehensible output to give response to other-initiation and self-initiation,which was studied in both NS-NNS and NNS-NNS interactions.It was the qualitative study by using two different tasks which were picture-dictation task and opinion-exchange task to collect the data.There were 32 participants whose age ranged of 22 to 37.The author proposed two hypotheses based on his expectation that NNS-NNS interactions would provide more opportunities for NNS participants to give comprehensible output for other-initiated clarification requests and self-initiated clarification attempts than NS-NNS interactions.The author was good at using numbers to illustrate and describe the data in his writing.

Analysis of Deaf Speakers’ Speech Signal for Understanding the Acoustic Characteristics by Territory Specific Utterances

An important concern with the deaf community is inability to hear partially or totally. This may affect the development of language during childhood, which limits their habitual existence. Consequently to facilitate such deaf speakers through certain assistive mechanism, an effort has been taken to understand the acoustic characteristics of deaf speakers by evaluating the territory specific utterances. Speech signals are acquired from 32 normal and 32 deaf speakers by uttering ten Indian native Tamil language words. The speech parameters like pitch, formants, signal-to-noise ratio, energy, intensity, jitter and shimmer are analyzed. From the results, it has been observed that the acoustic characteristics of deaf speakers differ significantly and their quantitative measure dominates the normal speakers for the words considered. The study also reveals that the informative part of speech in a normal and deaf speakers may be identified using the acoustic features. In addition, these attributes may be used for differential corrections of deaf speaker’s speech signal and facilitate listeners to understand the conveyed information.

Overview of Native-speaker English Teacher Versus Non-native-speaker English Teacher

As much more non-native-speaker English teachers teach alongside native-speaker English teachers, either in China or any other non-English-speaking country, research on the differences between native-speaker English teacher and non-native-speaker English teacher is necessary. This paper offers an overview of such difference between the two groups of English teachers in terms of their strengths and weaknesses, teaching styles and approaches. The conclusion suggests that cooperation and communication be emphsised and that the two groups of teachers communicate more and exchange their ideas on how to teach the same group of students more effectively.

A novel approach for speaker diarization system using TMFCC parameterization and Lion optimization

In audio stream containing multiple speakers, speaker diarization aids in ascertaining "who speak when". This is an unsupervised task as there is no prior information about the speakers. It labels the speech signal conforming to the identity of the speaker, namely, input audio stream is partitioned into homogeneous segments. In this work, we present a novel speaker diarization system using the Tangent weighted Mel frequency cepstral coefficient(TMFCC) as the feature parameter and Lion algorithm for the clustering of the voice activity detected audio streams into particular speaker groups. Thus the two main tasks of the speaker indexing, i.e., speaker segmentation and speaker clustering, are improved. The TMFCC makes use of the low energy frame as well as the high energy frame with more effect, improving the performance of the proposed system. The experiments using the audio signal from the ELSDSR corpus datasets having three speakers, four speakers and five speakers are analyzed for the proposed system. The evaluation of the proposed speaker diarization system based on the tracking distance, tracking time as the evaluation metrics is done and the experimental results show that the speaker diarization system with the TMFCC parameterization and Lion based clustering is found to be superior over existing diarization systems with 95% tracking accuracy.

Discourses of Chinese ELT Stakeholders on Native Speakerism

This paper reports on part of the findings of a large-scale study exploring the viewpoints of Chinese ELT stakeholders(students,teachers and administrators)on native speakerism in order to find out whether current EFL education in China is still affected by this chauvinistic ideology.The analysis of data via a critical lens reveals that the vast majority of the participants conferred upon NS products(teacher,language,culture and teaching methodology)a status superior to that granted to the NNS counterparts and failed to see linguacultural and epistemological inequalities between the English speaking West and traditional NNS countries,inter alia,China.These findings suggest that the three participant groups as an entirety succumb to native speakerism,and by extension that ELT in China is still haunted to a great degree by this ideology.Given that this study treats each participant group separately,future studies are expected to explore inter-group interactions in ideology.

Speaker Adaptation with Transformation Matrix Linear Interpolation

A transformation matrix linear interpolation (TMLI) approach for speaker adaptation is proposed. TMLI uses the transformation matrixes produced by MLLR from selected training speakers and the testing speaker. With only 3 adaptation sentences, the performance shows a 12.12% word error rate reduction. As the number of adaptation sentences increases, the performance saturates quickly. To improve the behavior of TMLI for large amounts of adaptation data, the TMLI+MAP method which combines TMLI with MAP technique is proposed. Experimental results show TMLI+MAP achieved better recognition accuracy than MAP and MLLR+MAP for both small and large amounts of adaptation data. Key words speech recognition - speaker adaptation - MLLR - MAP - maximum likelihood model interpolation (MLMI) CLC number TN 912. 34 Foundation item: Supported by the Science and Technology Committee of Shanghai (01JC14033)Biography: XU Xiang-hua (1977-), female, Ph. D. candidate, research direction: large vocabulary continuous Mandarin speech recognition and speaker adaptation

Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high accuracies.MFCCs represents a sequence of voice signal-specific features.This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings.Since the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D array.Several learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the speaker.The highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation function.The most important output of this study is the inclusion of human voice as a new feature set.

Locality Preserving Discriminant Projection for Speaker Verification

In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.

Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models

The aim of this paper is to show the accuracy and time results of a text independent automatic speaker recognition (ASR) system, based on Mel-Frequency Cepstrum Coefficients (MFCC) and Gaussian Mixture Models (GMM), in order to develop a security control access gate. 450 speakers were randomly extracted from the Voxforge.org audio database, their utterances have been improved using spectral subtraction, then MFCC were extracted and these coefficients were statistically analyzed by GMM in order to build each profile. For each speaker two different speech files were used: the first one to build the profile database, the second one to test the system performance. The accuracy achieved by the proposed approach is greater than 96% and the time spent for a single test run, implemented in Matlab language, is about 2 seconds on a common PC.

In the Age of ‘World Englishes’,Native Speaker Teachers or Non-native Speaker Teachers?

This paper attempts to argue that in the age of‘World Englishes', it is not necessary to differentiate native speaker teachers from non-native speaker teachers. It is concluded that non-native speaker teachers can be as effective as their native colleagues and they have equal chance to achieve professional success, even though native speaker teachers have great advantages over non-native teachers in some aspects. It is time for employers, as well as ELT professionals to shut their eyes to the glaring differences between native speaker teachers and non-native speaker teachers and optimize such unique resources.

The Potential Cultural Differences in Native-speaker Teacher's Language Classroom

While the majority of nonnative speaker English teachers teach alongside NS teachers,research on the role of native speaker English teachers in China's teaching context and the attitudes of university students towards them have been rarely conducted.This essay discusses the implications of cultural differences for the language classroom,and the different cultures of learning with regard to language teaching and learning in China and the Wes.The conclusion suggests that it is of great importance to have a good sense of cultural awareness and an open mind for cultural interactions,in order to benefit both language learners and native speaker teachers in the cross-cultural classroom.

How To Be A Good Speaker

Public speaking is a part of communication.Good public speaking can convey clear、 persuasive ideas or opinions and also can become an effective bridge between the audience and the speaker.This paper is dealing with some skills of public speaking- from several different aspects that should be noticed in public speaking.