By adding the speaker pruning part, the system recognition accuracy was increased 9. This study aims to explore the case of robust speaker recognition with multisession enrollments and noise, with an emphasis on optimal organization and utilization of speaker. Mfcc was first proposed for speech recognition and its melwarped frequency scale is to mimic how human ears process sound. Language recognition via ivectors and dimensionality reduction. Eigenvoice speaker adaptation via composite kernel pca james t. The eigenvoice and eigenchannel matrices were trained. Speaker recognition in a multispeaker environment alvin f martin, mark a. Pdf rapid speaker adaptation in eigenvoice space robust speech. Intuitively, compared to mllr, the eigenvoice speaker modeling puts strong restrictions on the speaker model. With the above analysis, we propose to combine eigenvoice speaker modeling and vtsbased environment compensation so as to do better speaker and noise factorization. In eigenvoice, the speaker acoustic space is described by a rectangular matrix.
An overview of textindependent speaker recognition. Each speaker factor vector is projected back to the supervector model space by the eigenvoice matrix e using 1, to rapidly synthesize. A speaker recognition system includes two primary components. An ivector extractor suitable for speaker recognition with both microphone and telephone speech mohammed senoussaoui 1. Score fusion takes advantage of the fact that different systems make different mistakes, and by combining their output scores, the overall system can reduce the dependence of output decisions on the mistakes of a particular. The upper is the enrollment process, while the lower panel illustrates the recognition process. Each eigenvoice models a direction of interspeaker variability.
The segmental eigenvoice method in 2 has been providing rapid speaker adaptation with limited. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. Deep learning is progressively gaining popularity as a viable alternative to ivectors for speaker recognition. Dsr front end lvcsr evaluation, au38402, aurora working group 2002 by n parihar, j picone add to metacart. Speech separation using speaker adapted eigenvoice speech models ron j. An ivector extractor suitable for speaker recognition with. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of human recognition orsag 2010. Pdf speaker identification and verification using gaussian mixture. The feature extraction module first transforms the raw signal into feature vectors in which speakerspecific properties are emphasized and statistical redundancies suppressed. Rapid speaker adaptation in eigenvoice space speech and. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on specific voices or it can be used to.
In 34 the eigenvoice approach has been applied effectively to the problem of modeling intraspeaker variability, by com pensating the session channel variability at recognition time. Abstract correlation between hmm parameters has been utilized for various rapid speaker adaptation, e. Speech separation using speakeradapted eigenvoice speech. During the project period, an english language speech database for speaker recognition elsdsr was built. This paper gives an overview of automatic speaker recognition technology, with an emphasis on text. Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. Home acm journals ieeeacm transactions on audio, speech and language processing vol. Pdf rapid speaker adaptation in eigenvoice space robust. In the enrollment mode, a speaker model is trained. Introduction measurement of speaker characteristics.
Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same. Incorporation of speech duration information in score fusion. Rapid speaker adaptation in eigenvoice space robust speech recognition article pdf available in ieee transactions on speech and audio processing 86. Speaker recognition from raw waveform with sincnet deepai.
Textdependent speaker recognition using plda with uncertainty propagation t. Reestimation processes are performed to more strongly separate speakerdependent and speakerindependent components of the speech model. The identity toolbox provides tools that implement both the conventional gmmubm and stateoftheart ivector based speaker recognition strategies. Input audio of the unknown speaker is paired against a group of selected speakers and in the case there is a match found, the speakers identity is returned. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same speaker is speaking. Automatic speaker recognition systems have a foundation built on ideas and techniques from the areas of speech science for speaker characterization, pattern recognition and engineering. The second part is the ddhmm speaker recognition performed on the survived speakers after pruning. Speaker recognition systems can be used to confirm or refuse that a person who is speaking is who he or she has indicated to be speaker verification and can also be used to determine who of a plurality of known persons is speaking speaker identification. Automatic speaker recognition algorithms in python. Eigenvoice speaker adaptation with minimal data for statistical speech synthesis systems using a map approach and nearestneighbors. Linear versus mel frequency cepstral coefficients for speaker. Motivated by this insight from speech production, this study compares the performances between mfcc and linear frequency cepstral coefficients lfcc in speaker recognition. Acoustic hole filling for sparse enrollment data using a. Reestimation processes are performed to more strongly separate speaker dependent and speaker independent components of the speech model.
The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of. Linear versus mel frequency cepstral coefficients for. The eigenvoice technique is also used during run time upon the speech of a new speaker. The basic assumption in eigenvoice modeling is that most of the eigenvalues of are zero. Ellis labrosa, department of electrical engineering, columbia university, 500 west 120th street, room 0. Speaker diarization based on bayesian hmm with eigenvoice. A compact representation of speakers in model space. Eigenvoice speaker adaptation with minimal data for. The role of age in factor analysis for speaker identification. Using eigenvoice coefficients as features in speaker recognition. Eigenvoice speaker adaptation has been shown to be effective in recent years. Dimensionality reduction techniques are al ready widely used in speech recognition. This incorporates kernel principal component analysis, a nonlinear version of principal component analysis, to capture higher order correlations in order to further explore the speaker space and enhance.
The role of age in factor analysis for speaker identi. Speaker recognition introduction speaker, or voice, recognition is a biometric modality that uses an individuals voice for recognition purposes. Speaker recognition using deep belief networks cs 229 fall 2012. Promising results have been recently obtained with convolutional neural networks cnns when fed by raw speech samples directly. Our first method is based on using a bayesian eigenvoice approach for constraining the adaptation algorithm to move in realistic directions in the speaker space to reduce artifacts. Speaker recognition is the identification of a person from characteristics of voices. Refer to comparison of scoring methods used in speaker recognition with joint factor analysis by glembek, et.
The approach constrains the adapted model to be a linear combination. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. A reduced dimensionality eigenvoice analytical technique is used during training to develop contextdependent acoustic models for allophones. The api can be used to determine the identity of an unknown speaker. This repository contains python programs that can be used for automatic speaker recognition. Matejka, speaker diarization based on bayesian hmm with eigenvoice priors, in proceedings of odyssey 2018, the speaker and language recognition workshop, 2018. Speaker recognition can be classified into identification and verification. Speaker diarization based on bayesian hmm with eigenvoice priors.
Robust speaker recognition system employing covariance. Speaker recognition from raw waveform with sincnet. Burget, analysis of variational bayes eigenvoice hidden markov model based speaker diarization, to be published, 2019. Classification methods for speaker recognition springerlink. In 34 the eigenvoice approach has been applied effectively to the problem of modeling intra speaker variability, by com pensating the session channel variability at recognition time. Here, we propose three methods to alleviate the quality problems of the baseline eigenvoice adaptation algorithm while allowing speaker adaptation with minimal data.
Eigenvoice modeling with sparse training data article pdf available in ieee transactions on speech and audio processing 3. Asr is done by extracting mfccs and lpcs from each speaker and then forming a speakerspecific codebook of the same by using vector quantization i like to think of it as a fancy. Linear versus mel frequency cepstral coefficients for speaker recognition xinhui zhou, daniel garciaromero ramani duraiswami, carol espywilson shihab shamma university of maryland, college park asru 2011. Us20030046068a1 eigenvoice reestimation technique of. Pdf this paper describes a new modelbased speaker adaptation. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. Robust speaker recognition system employing covariance matrix and eigenvoice conference paper in midwest symposium on circuits and systems august 20 with 11 reads how we measure reads. It is an important topic in speech signal processing and has a variety of applications, especially in security systems. Eigenvoice used in speaker recognition with a few training.
Experimental results for a smallvocabulary task letter recognition given. Incorporation of speech duration information in score. Eigenvoice speaker adaptation via composite kernel. In this chapter we provide an overview of the features, models, and classifiers derived from these areas that are the basis for modern automatic speaker. But system description for dihard speech diarization. Abstract recently, we proposed an improvement to the conventional eigenvoice ev speaker adaptation using kernel methods. In this paper, we propose to use eigenvoice coefficients as features for speaker recognition. A possible solution is the eigenvoice estimate clients approach, in which. The term voice recognition can refer to speaker recognition or speech recognition. Glottis lips tongue linear versus mel frequency cepstral. In our novel kernel eigenvoice kev speaker adaptation 1, speaker supervectors. It can be used for authentication, surveillance, forensic speaker recognition and a. Using eigenvoice coefficients as features in speaker. In the side of adapting in speaker recognition system modeling, we will ameliorate conventional map maximum a posterior probability means to get speaker recognition model, apply mllr maximum likelihood linear regression and eigenvoice adaptation ways which used in speech recognition into adapting in speaker recognition system modeling, and.
Kwok abstract recently, we proposed an improvement to the conventional eigenvoice ev speaker adaptation using kernel methods. An ivector extractor suitable for speaker recognition. Latent correlation analysis of hmm parameters for speech. Voice controlled devices also rely heavily on speaker recognition. Speech separation using speakeradapted eigenvoice speech models. Asr is done by extracting mfccs and lpcs from each speaker and then forming a speaker specific codebook of the same by using vector quantization i like to think of it as a fancy. A possible solution is the eigenvoice approach, in which client and test speaker models are confined to a lowdimensional linear subspace obtained previously. Rapid speaker adaptation in eigenvoice space roland kuhn, jeanclaude junqua, member, ieee, patrick nguyen, and nancy niedzielski abstract this paper describes a new modelbased speaker adaptation algorithm called the eigenvoice approach. Speech separation using speakeradapted eigenvoice speech models ron j. Language recognition via ivectors and dimensionality. Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting brian mak, roger hsiao, simon ho, and james t. In eigenvoice training for speaker recognition, all the recordings of a given speaker are considered to belong to the same person.
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