An innovative approach is carried out here is voice authentication and it is a procedure of validating individual’s identity by recognizing their voice. It is often called as speaker verification. We can carry out several applications in this domain like call centers, mobile apps safety, smart home devices etc.
Under your PhD or MS research we intend to gather all our findings related to your Voice Authentication Using Machine Learning research approach, methods that we use, discuss its similarities, differences, from other previous research and give a brief explanation to scholars’ all types of research work right from topic selection to paper publication are handled by us effectively.
Now, we discuss about the processing flow of developing voice authentication model by utilizing machine learning techniques:
- Objective Description:
- To authenticate the individual by considering their voice, we develop a model.
- Collection of data:
- Voice Data: Efficient amount of voice data are gathered by us and we should check whether the data comprises of any background sounds, various phrases and tones.
- Public Datasets: Even the dataset named VoxCeleb is not convenient for voice authentication; initially we utilized this in our work.
- Preprocessing of data:
- Segmentation: We performed segmentation process to minimize the huge audio data into small data.
- Feature Extraction: Libraries such as Librosa are employed by us for feature extraction procedure to retrieve relevant features such as Mel-scaled Spectrogram, Tonnetz, Mel-Frequency Cepstral Coefficients (MFCC), Contrast and Chroma.
- Noise Minimization: To eliminate the background sounds, noise minimization approaches are used in our research.
- Feature Engineering:
- We integrate various distinct feature data and evaluate that to discover the optimal feature set based on the findings.
- Model Chosen and training:
- For validation tasks, we utilize Siamese Networks or Triplet Loss Networks and these can learn embeddings which are very far for the distinct speakers and for identical speaker, it is nearer.
- Various methods are also applied by us such as Deep Neural Networks (DNN) and Gaussian Mixture Models (GMM).
- Only a required part of data is utilized for training process and the remaining data used for validation process.
- False Rejection Rate (FRR) & False Acceptance Rate (FAR): We evaluated the model’s authenticity and accuracy.
- Equal Error rate (EER): It denotes the value at which the FAR value equals the FRR value. When we attained minimum EER value, our model is expressed as an efficient one.
- When we are required to do an actual time validation, an appropriate environment needs to be selected to implement our model. For mobile or IoT device related applications, we can choose edge deployment.
- If our model is utilized in sensitive platforms, we need to check all the safety protocols.
- Continuous Learning:
- Often our framework must be enhanced through a latest data to manage the voice modifications that happen because of various attributes like health, age or others.
- Spoofing Assaults: To illegally unlock the system, the intruders may utilize already recorded voice data.
- Voice variability: Because of several factors like background sounds, mindset and illness, one’s voice may differ.
- Data Privacy: We consider safety measures while gathering and storing the voice data due to its sensitive nature.
- Multimodal Authentication: To improve the confidentiality, we integrate other biometrics like face or fingerprint with voice authentication.
- Anti-spoofing Measures: Liveness identification model can deploy to ensure the reliability of the voice by checking whether the voice is from a recorded data or from a speaker in live.
Libraries & Tools:
- Datasets: We utilize different datasets such as common voice by Mozilla or VoxCeleb.
- Python Libraries: For conventional ML model, we can use Scikit-learn library. For DL, PyTorch or TensorFlow can be utilized. For audio processing, we make use of Librosa.
If we are aiming to develop voice authentication model, we should always aware of the possible safety attacks and implement some security measures to overcome them. The model’s effectiveness and authenticity can be improved if we associate with professionals in cyber-security and audio-based researches.
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Voice Authentication Using Machine Learning Research Thesis Topics
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- A Hybrid Mel Frequency Cepstral Coefficients and Bayesian Gaussian Mixture Model for Voice based Authentication Websites
Bayesian Gaussian Mixture model, HMFCC, voice authentication
This article developed a voice-based authentication framework to access websites. User’s voice signals are initially collected for the registration process. To train the voice authentication framework, the Hybrid Mel Frequency Cepstral Coefficients and Bayesian Gaussian Mixture Model is proposed. Utilizing MFCC, features are extracted from voice and the framework is trained by employing BGMM. Results show that, HMFCC-BGMM provides better outcomes.
- Study of Adam and Adamax Optimizers on AlexNet Architecture for Voice Biometric Authentication System
Adam Optimizer, Adamax Optimizer, AlexNet Architecture
The CNN architecture, AlexNet is utilized in this study that is suitable for small amount of dataset. In Deep learning methods, optimization techniques are very essential, for that, this study used the voice dataset to examine whether Adam or AdaMax is the best optimizer or not for AlexNet architecture. As a result, AdaMax optimizer achieved highest outcomes than Adam.
- Accuth+: Accelerometer-based Anti-Spoofing Voice Authentication on Wrist-worn Wearables
Accelerometers, Wearable computers, Vibrations, Loudspeakers, Microphones, Feature extraction
Accuth+ is a new authentication model on wrist-worn device that is proposed in this paper. This model takes merits of minimal cost accelerometer to validate the user’s identity and avoid spoofing acoustic assaults. It analyzes specific sound vibration at the human pronunciation procedure and extracts various features for user’s identity validation. To guard against spoofing assaults, variations among physical and loudspeaker sound features are analyzed.
- Real time implementation of voice based robust person authentication using T-F features and CNN
Forensic investigation, Spectrogram, CNN, Machine learning, Person authentication, Raspberry Pi hardware
To identify individuals, recorded voice data are utilized in this study. From the combined training data of human utterances, Time-frequency (T-F) features are obtained that are given to CNN with layers designed for developing templates. Recognition accuracy is evaluated based on the match determined to verify the feature selection and CNN method. Integration of features with CNN for modeling and classification provides efficient authentication rate.
- VOGUE: Secure User Voice Authentication on Wearable Devices using Gyroscope
Replay attack, speech movement sequence, gyroscope
To differentiate among registered authentic user and malicious attackers, this study suggested VOGUE that captures identical and stable range of speech movement with inserting gyroscope in wearable devices. The main observations of VOGUE is, First it will analyze facial actions while speaking and the second thing is, it will examine the generation of specific words. VOGUE is executed in various android devices like smart glasses, watches and phones.
- Voice Based Authentication Using Mel-Frequency Cepstral Coefficients and Gaussian Mixture Model
Voice Recognition model, Mel-Frequency Cepstral Coefficients (MFCC), Gaussian Mixture Model (GMM)
Various attacks like replay, personification and assaults utilizing AI voice bots and demerits such as text and language dependency of human voice authentication framework are addressed in this study. We created an efficient model to overcome these issues. By matching voices, the individuals are validated and also some queries to be asked to the users that can’t be answered by any AI bot.
- VoiceSketch: a Privacy-Preserving Voiceprint Authentication System
User Authentication, Voiceprint Authentication, Template Protection
A confidential voiceprint authentication model named VoiceSketch is recommended in this article that is based on secure sketch to safeguard the confidentiality of individual voiceprint template. It utilized secure sketch to correct faults among similar user’s various voice features. To extract authentication key from an individual’s voice template, cryptographic hash function is employed. From the use of secure sketch and hash function, safety of Voice Sketch is examined.
- Navigate-Me: Secure voice authenticated indoor navigation system for blind individuals
Vision impaired, localization, indoor navigation
A new Navigate-Me approach for indoor navigation designed for visually impaired persons is proposed in this study with high safety and reliability. It comprises, White Cane with sensors for obstacle identification, Bluetooth beacon integration for localization and ML model for voice authentication. An algorithm protocol is used for secure interaction among server and application to direct the visually impaired persons in moving to known and unknown places.
- Impostor Recognition Based Voice Authentication by Applying Three Machine Learning Algorithms
Speaker Authentication, SVM, LR, ONE.R, Impostor
To detect frauds, various ML techniques such as Support Vector Machine (SVM), One Rule (One-R), Linear Regression (LR) are utilized in this study for voice authentication. Preprocessing of audio is performed such as noise minimizing and voiced improving to enhance the quality of audios. From all audio metrics, MFCC and various features are extracted. As a consequence, SVM outperforms other methods.
- A Review of Recent Machine Learning Approaches for Voice Authentication Systems
Audio signal processing, Security attacks
In this article, various single-modal and multimodal voice authentication researches are reviewed. It also describes about several feature extraction and classification techniques. Various security assaults on voice authentication framework are discussed in this paper such as hidden voice command assaults, random assaults, voice synthesizing assaults, mimicry assaults, counterfeit assaults and replay assaults.