Eigenvoice speaker adaptation with minimal data for statistical speech synthesis systems using a map approach and nearestneighbors. Rapid speaker adaptation in eigenvoice space speech and. The feature extraction module first transforms the raw signal into feature vectors in which speakerspecific properties are emphasized and statistical redundancies suppressed. 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. Burget, analysis of variational bayes eigenvoice hidden markov model based speaker diarization, to be published, 2019. Experimental results for a smallvocabulary task letter recognition given. Home acm journals ieeeacm transactions on audio, speech and language processing vol. Pdf rapid speaker adaptation in eigenvoice space robust. Intuitively, compared to mllr, the eigenvoice speaker modeling puts strong restrictions on the speaker model. The eigenvoice and eigenchannel matrices were trained. But system description for dihard speech diarization. Speaker diarization based on bayesian hmm with eigenvoice priors. Reestimation processes are performed to more strongly separate speaker dependent and speaker independent components of the speech model.
A possible solution is the eigenvoice approach, in which client and test speaker models are confined to a lowdimensional linear subspace obtained previously. Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting brian mak, roger hsiao, simon ho, and james t. Using eigenvoice coefficients as features in speaker recognition. Us20030046068a1 eigenvoice reestimation technique of. Rapid speaker adaptation in eigenvoice space robust speech recognition article pdf available in ieee transactions on speech and audio processing 86. Eigenvoice modeling with sparse training data article pdf available in ieee transactions on speech and audio processing 3. A compact representation of speakers in model space. 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. Speech separation using speakeradapted eigenvoice speech models ron j. The upper is the enrollment process, while the lower panel illustrates the recognition process. 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. It is an important topic in speech signal processing and has a variety of applications, especially in security systems.
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. In eigenvoice, the speaker acoustic space is described by a rectangular matrix. With the above analysis, we propose to combine eigenvoice speaker modeling and vtsbased environment compensation so as to do better speaker and noise factorization. Classification methods for speaker recognition springerlink. The identity toolbox provides tools that implement both the conventional gmmubm and stateoftheart ivector based speaker recognition strategies. Motivated by this insight from speech production, this study compares the performances between mfcc and linear frequency cepstral coefficients lfcc in speaker recognition. In this paper, we propose to use eigenvoice coefficients as features for speaker recognition. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of human recognition orsag 2010. The role of age in factor analysis for speaker identification. Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al.
An ivector extractor suitable for speaker recognition with. Linear versus mel frequency cepstral coefficients for speaker. Speaker recognition can be classified into identification and verification. Promising results have been recently obtained with convolutional neural networks cnns when fed by raw speech samples directly. Speech separation using speakeradapted eigenvoice speech. Dimensionality reduction techniques are al ready widely used in speech recognition. Speech separation using speakeradapted eigenvoice speech models. A possible solution is the eigenvoice estimate clients approach, in which. Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice kev is proposed. Speaker diarization based on bayesian hmm with eigenvoice. 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.
Speaker recognition in a multispeaker environment alvin f martin, mark a. In our novel kernel eigenvoice kev speaker adaptation 1, speaker supervectors. Each eigenvoice models a direction of interspeaker variability. The basic assumption in eigenvoice modeling is that most of the eigenvalues of are zero. A reduced dimensionality eigenvoice analytical technique is used during training to develop contextdependent acoustic models for allophones. 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. 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. 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. Pdf speaker identification and verification using gaussian mixture. Matejka, speaker diarization based on bayesian hmm with eigenvoice priors, in proceedings of odyssey 2018, the speaker and language recognition workshop, 2018.
The second part is the ddhmm speaker recognition performed on the survived speakers after pruning. Voice controlled devices also rely heavily on speaker recognition. Introduction measurement of speaker characteristics. The role of age in factor analysis for speaker identi. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. 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. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same speaker is speaking. Pdf rapid speaker adaptation in eigenvoice space robust speech. In the enrollment mode, a speaker model is trained. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. Abstract recently, we proposed an improvement to the conventional eigenvoice ev speaker adaptation using kernel methods. Acoustic hole filling for sparse enrollment data using a.
Language recognition via ivectors and dimensionality. Eigenvoice speaker adaptation has been shown to be effective in recent years. Refer to comparison of scoring methods used in speaker recognition with joint factor analysis by glembek, et. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of. A speaker recognition system includes two primary components.
Pdf this paper describes a new modelbased speaker adaptation. Kwok abstract recently, we proposed an improvement to the conventional eigenvoice ev speaker adaptation using kernel methods. 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. During the project period, an english language speech database for speaker recognition elsdsr was built. Mfcc was first proposed for speech recognition and its melwarped frequency scale is to mimic how human ears process sound. The approach constrains the adapted model to be a linear combination. This paper gives an overview of automatic speaker recognition technology, with an emphasis on text. Using eigenvoice coefficients as features in speaker. 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. Speaker recognition introduction speaker, or voice, recognition is a biometric modality that uses an individuals voice for recognition purposes. An ivector extractor suitable for speaker 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.
Dsr front end lvcsr evaluation, au38402, aurora working group 2002 by n parihar, j picone add to metacart. We apply the approach to speaker adaptation and speaker recognition. Eigenvoice speaker adaptation via composite kernel pca james t. An ivector extractor suitable for speaker recognition with both microphone and telephone speech mohammed senoussaoui 1. 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. Textdependent speaker recognition using plda with uncertainty propagation t. This repository contains python programs that can be used for automatic speaker recognition. Speaker recognition in a multi speaker environment alvin f martin, mark a. Automatic speaker recognition algorithms in python. Reestimation processes are performed to more strongly separate speakerdependent and speakerindependent components of the speech model.
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 api can be used to determine the identity of an unknown speaker. Glottis lips tongue linear versus mel frequency cepstral. Language recognition via ivectors and dimensionality reduction. Eigenvoices for speaker adaptation semantic scholar.
Speech separation using speaker adapted eigenvoice speech models ron j. 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. Ellis labrosa, department of electrical engineering, columbia university, 500 west 120th street, room 0. Speaker recognition from raw waveform with sincnet deepai. The eigenvoice technique is also used during run time upon the speech of a new speaker.
Speaker recognition is the identification of a person from characteristics of voices. Deep learning is progressively gaining popularity as a viable alternative to ivectors for speaker recognition. Here, we propose three methods to alleviate the quality problems of the baseline eigenvoice adaptation algorithm while allowing speaker adaptation with minimal data. By adding the speaker pruning part, the system recognition accuracy was increased 9. Speaker recognition using deep belief networks cs 229 fall 2012. In eigenvoice training for speaker recognition, all the recordings of a given speaker are considered to belong to the same person. Linear versus mel frequency cepstral coefficients for. Eigenvoice speaker adaptation with minimal data for. Speaker recognition from raw waveform with sincnet. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. Abstract correlation between hmm parameters has been utilized for various rapid speaker adaptation, e. It can be used for authentication, surveillance, forensic speaker recognition and a. The segmental eigenvoice method in 2 has been providing rapid speaker adaptation with limited.
It is no doubt that the performance of speech recognition is significantly degraded by. Eigenvoice speaker adaptation via composite kernel. 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. Robust speaker recognition system employing covariance. Eigenvoice used in speaker recognition with a few training. 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. Each speaker factor vector is projected back to the supervector model space by the eigenvoice matrix e using 1, to rapidly synthesize.
Incorporation of speech duration information in score fusion. The term voice recognition can refer to speaker recognition or speech recognition. Incorporation of speech duration information in score. An overview of textindependent speaker recognition. Latent correlation analysis of hmm parameters for speech. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin.
306 119 1121 421 331 454 740 1035 528 691 401 690 1369 1053 589 1301 1035 995 22 798 287 1217 892 1106 807 1367 759 1128 1394 806 1222 813 240