A Survey on “Speaker Recognition System”

Under the Guidance of :

Prof.S.M.Hatture

Objectives…

• Introduction• Speaker Recognition• Literature Survey• Issues and Challenges• Conclusion

Introduction

• To create new services that will make our every day lives more secured.

• For forensic purposes.

Speaker Recognition

Literature Survey

1. CASA-Based Robust Speaker Identification

(Computational Auditory Scene Analysis)

2. Independent component analysis and MLLR transformation for speaker identification

• Independent Component Analysis (ICA).• Principle Component Analysis (PCA).

3. Towards noise –robust speaker recognition using probabilistic linear discriminant analysis

• Probabilistic linear discriminant analysis• Additive noise

4. Weighted LDA techniques for i-vector based speaker verification.

• Improving i-vector speaker verification in presence of high inter session variability.

• Interview-interview condition.• Telephone-telephone condition.

5. An Overview of Speaker Identification: Accuracy and Robustness IssuesTwo methods

• Speaker identification• Speaker verification

6. Cross-pollination of normalization techniques from speaker to face authentication using Gaussian mixture models.

7.Front-End Factor Analysis for Speaker Verification

• This paper proposed new way of combining JFA and SVM’s for speaker verification.

8. Parallel transformation network feature for speaker recognition

• TN features with SVM modeling-method in order to become language independent and overcome the need for accurate speech recognition.

9. Statistical Pattern Recognition Techniques for Speaker Verification

10. Speaker Identification within Whispered Speech Audio Streams Whisper is an alternative speech production mode used by subjects in natural conversation to protect the privacy. Whispered speech is a natural mode of speech information.

11. A comparison of approaches for modeling prosodic features in speaker recognition.

• It address the task of text-independent speaker verification.

• Prosodic features.

12.Fusion Methods for Boosting Performance of Speaker Identification Systems

1. feature extraction.

2.classification tasks.

13. Source-normalized LDA for robust speaker recognition using i-vectors from multiple speech sources

• Improves the robustness of i-vector-based speaker recognition.

• An source-normalized algorithm to improves robustness of i-vector-based-speaker recognition.

14. A study on Universal Background Model training in Speaker Verification

• Systematic analyze of speaker verification system performance.

• Rigorous methods like IFS scheme is used to estimate similarity.

15. Speaker Identification Using Instantaneous Frequencies • Introduction of new set of descriptors that capture the

identity of speaker well.• Provides robustness with respect to changes in

recording channel and speaking style.

16. Codebook Design Method for Noise Robust Speaker Identification based on Genetic Algorithm

• To designing a codebook for noise robust speaker, Genetic algorithm is proposed.

Paradigm of the proposed codebook design method.

17. Enhanced speaker recognition based on intra-modal fusion and accent modeling.

• Intra-modal fusion.• Accent modeling.

18. Discriminant NAP for SVM Speaker Recognition

• Nuisance Attribute Projection (NAP) provides an effective method of removing the unwanted session variability in a Support Vector Machine (SVM) based speaker recognition system by removing the principal components of this variability.

19. A Speech-and-Speaker Identification System: Feature Extraction, Description and Classification of Speech-Signal Image

• A speech-and-speaker (SAS) identification system based on spoken Arabic digit recognition.

20. In-Set/Out-of-Set Speaker Recognition Under Sparse Enrollment

• The problem of in-set speaker recognition is addressed with the constraints of low enrollment (5 s) and test material (2–8 s) and in-set group sizes ranging from 15–45 speakers.

• An algorithm is proposed that uses an in-set speaker’s cohort set to make up for the sparse (e.g., 5 s per speaker) enrollment data.

21. Analysis of Speech Recognition Techniques for use in a Non-Speech Sound Recognition System

• Analysis the different techniques used for speech recognition and identifies those that can be used for non-speech sound recognition

22. Speaker verification for home security system

• A reliable speaker verification algorithm is used in home security.

23. An Efficient Scoring Algorithm for Gaussian Mixture Model Based Speaker Identification

• The use of GMM for speaker identification was shown to provide superior performance

Graphical illustration of the observation vector recording

24. Speaker Recognition: A Tutorial

• Speech processing is a diverse field with many applications.

25. Speaker Identification Based on the Use of Robust Cepstral Features Obtained from Pole-Zero Transfer Functions

• An attempt made to alleviate mismatch in the training and testing conditions.

• Proposed a new feature called linear predictive ceptrum derived by pole-zero function.

26. Speaker Verification Using Mixture Decomposition Discrimination

• Mixture decomposition discrimination (MDD) is based on the idea that, when modeling speech using hidden Markov models (HMM), different speakers speaking the same word would cause different HMM mixture components to dominate.

27. Recent Advances in the Automatic Recognition of Audiovisual Speech

28. Unsupervised Speaker Recognition Based on Competition Between Self-Organizing Maps

• Clustering the speaker from unlabeled and unsegmented conversation, when no priori knowledge about the identity of the participants is given.

29. Speaker Recognition with Polynomial Classifiers

• Polynomial –based classifier to achieve high accuracy at low complexity.

- It has several advantages. 1. Polynomial classifier scoring yields a system which is highly

computationally scalable with the number of speakers. 2. A new training algorithm is proposed which is discriminative,

handles large data sets, and has low memory usage. 3. The output of the polynomial classifier is easily incorporated into

a statistical framework allowing it to be combined with other techniques such as HMM.

30. Automatic Verbal Information Verification for User AuthenticationAn example of verbal information verification by asking sequential questions.

Issues and challenges…

• Robustness• Portability• Adaptation• Language modeling• Confidence measure• Out of vocabulary words• Prosody

Conclusion…

• Problems are still with speaker-generated variability and variability in channel and recording conditions.

• It is very important to investigate feature parameters that are stable over time, insensitive to the variation of speaking manner, including the speaking rate and level, and robust against variations in voice quality due to causes such as voice disguise or colds.

• Studies on ways to automatically extract the speech periods of each person separately from a dialogue involving more than two people have recently appeared as an extension of speaker recognition technology.

Thank You…