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Improving Robustness of Speaker Recognition using Discriminative Techniques
Novotný, Ondřej ; Ferrer, Luciana (oponent) ; Pollák, Petr (oponent) ; Černocký, Jan (vedoucí práce)
This work deals with discriminative techniques in speaker verification systems to improve robustness of the systems against factors that negatively affect their performance. These factors include noise, reverberation, or the transmission channel. The thesis consists of two main parts. In the first part, it deals with a theoretical introduction to current state-of-the-art speaker verification systems. The recognition system's steps are described, starting from the extraction of acoustic features, the extraction of vector representations of recordings, and the final recognition score computation. Particular emphasis is paid to the techniques of extraction of a vector representation of a recording, where we describe two different paradigms: the i-vectors and the x-vectors. The second part of the work focuses more on discriminative techniques to increase robustness. Their description is organized to match the gradual passage of the recording through the verification system. First, attention is paid to signal pre-processing using a neural network for noise reduction and speech enhancement. This pre-processing is a universal technique independent of the verification system. The work follows by focusing on the use of a discriminative approach in the extraction of features and the extraction of vector representations of recordings. Furthermore, this work sheds light on the transition from generative systems to discriminative systems. In order to give a fuller context, the work also describes techniques that had historically preceded this transition. All presented techniques are always experimentally verified and their advantages evaluated. We are proposing several techniques that have proved successful in both the generative approach in the form of i-vectors and discriminative x-vectors, and thanks to them, considerable improvement has been achieved. For completeness, in the field of robustness, other techniques are included in the work, such as normalization of scores or multi-condition training. Finally, the work deals with the robustness of discriminative systems in terms of data used in their training.
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Robustní rozpoznávání mluvčího
Profant, Ján ; Novotný, Ondřej (oponent) ; Matějka, Pavel (vedoucí práce)
Cílem této práce je analyzovat úspěšnost systému rozpoznávaní mluvčího na nahrávkach degradovaných různym telefonním přenosovým kanálem. Použili jsme dva způsoby extrakce příznaků - Mel Frequency Cepstral Coefficients (MFCC) a moderní systém, který spojuje Bottleneck příznaky spolu s MFCC. Systém rozpoznávání mluvčího je založen na i-vektorech a Pravděpodobnostní Lineární Diskriminační Analýze (PLDA). Porovnali jsme scenáře, kde je PLDA trénovaná jen na čisté řeči, poté systém kde jsme přidali data s hlukem a reverberací a nakonec, data degradované kodekem. Vyhodnotili jsem systémy za rovnakých podmínek (data ze stejného kodeku byli také v trénování PLDA) a také za rozdílnych podmínek (data ze stejného kodeku resp. rodiny kodeků nebyli v trénování PLDA). Také jsme experimentovali s nedávno představenou technikou na adaptaci kanálu - Within-class Covariance Correction (WCC). Můžeme jednoznačně vidět zlepšení úspěšnosti přidáním degradovaných dat do PLDA resp. WCC (s přibližně stejným výsledkem) pro obě naše testované podmínky.
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Detekce Akustického Prostředí z Řeči
Dobrotka, Matúš ; Glembek, Ondřej (oponent) ; Matějka, Pavel (vedoucí práce)
Téma tejto diplomovej práce je klasifikácia audio nahrávky do 15 tried akustických prostredí, v ktorých sa ľudia bežne nachádzajú. Práca popisuje 2 metódy založené na GMM a i-vektoroch a ich vzájomnú fúziu. Na dátach zo súťaže DCASE dosiahol najlepší GMM systém úspešnosť 60.4% a i-vektor systém 68.4%. Fúzia GMM systému a najlepšieho i-vektor systému výsledok ešte zlepšila na 69.3%, čo by v dobe súťaže stačilo na 20. miesto z 98 odovzdaných systémov z celého sveta.
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Microphone Arrays for Speaker Recognition
Mošner, Ladislav ; Plchot, Oldřich (oponent) ; Černocký, Jan (vedoucí práce)
This thesis addresses the problem of remote speaker recognition. The accuracy of standard speaker recognition decreases considerably in the presence of far-field data, therefore, we devised two strategies to improve the results. First, we employed a microphone array (purposely positioned set of microphones) that is able to steer a virtual "beam" to the position of the speaker. We also performed system adaptation of different parts of the system (PLDA scoring and i-vector extraction). We have synthesized our training and test data from the standard NIST 2010 data by room simulation and we have shown that both techniques and their combination significantly improve the results. We have also dealt with joint speaker identity and position estimation. While the results in simulated outdoor environment (reverberation-free) are encouraging, the results from interiors (with reverberation) are mixed and require further investigation. Finally, we were able to test our system on a limited amount of real re-transmitted data. While the results for male speakers match the simulation, the results for females are not convincing and need further analysis.
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Improving Robustness of Speaker Recognition using Discriminative Techniques
Novotný, Ondřej ; Ferrer, Luciana (oponent) ; Pollák, Petr (oponent) ; Černocký, Jan (vedoucí práce)
This work deals with discriminative techniques in speaker verification systems to improve robustness of the systems against factors that negatively affect their performance. These factors include noise, reverberation, or the transmission channel. The thesis consists of two main parts. In the first part, it deals with a theoretical introduction to current state-of-the-art speaker verification systems. The recognition system's steps are described, starting from the extraction of acoustic features, the extraction of vector representations of recordings, and the final recognition score computation. Particular emphasis is paid to the techniques of extraction of a vector representation of a recording, where we describe two different paradigms: the i-vectors and the x-vectors. The second part of the work focuses more on discriminative techniques to increase robustness. Their description is organized to match the gradual passage of the recording through the verification system. First, attention is paid to signal pre-processing using a neural network for noise reduction and speech enhancement. This pre-processing is a universal technique independent of the verification system. The work follows by focusing on the use of a discriminative approach in the extraction of features and the extraction of vector representations of recordings. Furthermore, this work sheds light on the transition from generative systems to discriminative systems. In order to give a fuller context, the work also describes techniques that had historically preceded this transition. All presented techniques are always experimentally verified and their advantages evaluated. We are proposing several techniques that have proved successful in both the generative approach in the form of i-vectors and discriminative x-vectors, and thanks to them, considerable improvement has been achieved. For completeness, in the field of robustness, other techniques are included in the work, such as normalization of scores or multi-condition training. Finally, the work deals with the robustness of discriminative systems in terms of data used in their training.
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Detekce Akustického Prostředí z Řeči
Dobrotka, Matúš ; Glembek, Ondřej (oponent) ; Matějka, Pavel (vedoucí práce)
Téma tejto diplomovej práce je klasifikácia audio nahrávky do 15 tried akustických prostredí, v ktorých sa ľudia bežne nachádzajú. Práca popisuje 2 metódy založené na GMM a i-vektoroch a ich vzájomnú fúziu. Na dátach zo súťaže DCASE dosiahol najlepší GMM systém úspešnosť 60.4% a i-vektor systém 68.4%. Fúzia GMM systému a najlepšieho i-vektor systému výsledok ešte zlepšila na 69.3%, čo by v dobe súťaže stačilo na 20. miesto z 98 odovzdaných systémov z celého sveta.
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Microphone Arrays for Speaker Recognition
Mošner, Ladislav ; Plchot, Oldřich (oponent) ; Černocký, Jan (vedoucí práce)
This thesis addresses the problem of remote speaker recognition. The accuracy of standard speaker recognition decreases considerably in the presence of far-field data, therefore, we devised two strategies to improve the results. First, we employed a microphone array (purposely positioned set of microphones) that is able to steer a virtual "beam" to the position of the speaker. We also performed system adaptation of different parts of the system (PLDA scoring and i-vector extraction). We have synthesized our training and test data from the standard NIST 2010 data by room simulation and we have shown that both techniques and their combination significantly improve the results. We have also dealt with joint speaker identity and position estimation. While the results in simulated outdoor environment (reverberation-free) are encouraging, the results from interiors (with reverberation) are mixed and require further investigation. Finally, we were able to test our system on a limited amount of real re-transmitted data. While the results for male speakers match the simulation, the results for females are not convincing and need further analysis.
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Robustní rozpoznávání mluvčího
Profant, Ján ; Novotný, Ondřej (oponent) ; Matějka, Pavel (vedoucí práce)
Cílem této práce je analyzovat úspěšnost systému rozpoznávaní mluvčího na nahrávkach degradovaných různym telefonním přenosovým kanálem. Použili jsme dva způsoby extrakce příznaků - Mel Frequency Cepstral Coefficients (MFCC) a moderní systém, který spojuje Bottleneck příznaky spolu s MFCC. Systém rozpoznávání mluvčího je založen na i-vektorech a Pravděpodobnostní Lineární Diskriminační Analýze (PLDA). Porovnali jsme scenáře, kde je PLDA trénovaná jen na čisté řeči, poté systém kde jsme přidali data s hlukem a reverberací a nakonec, data degradované kodekem. Vyhodnotili jsem systémy za rovnakých podmínek (data ze stejného kodeku byli také v trénování PLDA) a také za rozdílnych podmínek (data ze stejného kodeku resp. rodiny kodeků nebyli v trénování PLDA). Také jsme experimentovali s nedávno představenou technikou na adaptaci kanálu - Within-class Covariance Correction (WCC). Můžeme jednoznačně vidět zlepšení úspěšnosti přidáním degradovaných dat do PLDA resp. WCC (s přibližně stejným výsledkem) pro obě naše testované podmínky.
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