Password | EPFL Graph Search

A password, sometimes called a passcode (for example in Apple devices), is secret data, typically a string of characters, usually used to confirm a user's identity. Traditionally, passwords were expected to be memorized, but the large number of password-protected services that a typical individual accesses can make memorization of unique passwords for each service impractical. Using the terminology of the NIST Digital Identity Guidelines, the secret is held by a party called the claimant while the party verifying the identity of the claimant is called the verifier. When the claimant successfully demonstrates knowledge of the password to the verifier through an established authentication protocol, the verifier is able to infer the claimant's identity. In general, a password is an arbitrary string of characters including letters, digits, or other symbols. If the permissible characters are constrained to be numeric, the corresponding secret is sometimes called a persona

Joint speech and speaker recognition

The goal of the thesis is to investigate different approaches that combine and integrate Automatic Speech Recognition (ASR) and Speaker Recognition (SR) systems, with applications to (1) User-Customized Password Speaker Verification (UCP-SV) systems, and, (2) joint speech and speaker recognition. Unlike text-dependent speaker verification systems, in UCP-SV systems, customers can choose easily their own password, which has to be pronounced a few times during enrollment to create a customer specific model that will be subsequently used for verification. The main assumption in such systems is that no a priori knowledge about the password (such as its phonetic transcription) is available. However, although more user-friendly and more secure, UCP-SV systems are less understood and actually exhibit several new challenges, including: automatic inference of Hidden Markov Model (HMM) password (using a speaker-independent ASR system), fast speaker adaptation of the resulting acoustic models, score normalization, and verification of both lexical and speaker characteristics. Development and evaluation of such systems are then based on their ability to jointly verify: (1) the identity of a claimed speaker, (2) pronouncing the correct password, and thus rejecting all other possible alternatives. In this thesis, two different speaker acoustic modeling approaches are investigated: HMM/GMM approach (based on Gaussian Mixture Model, GMM) and hybrid HMM/MLP approach (based on Multi-Layer Perceptron, MLP). In the case of HMM/GMM approach, the background model used for likelihood normalization was the main difficulty, and several solutions were investigated to improve the baseline system. In the HMM/MLP approach, MLP adaptation was also a problem. In that context, we found that the modeling capability of the adapted MLP was more towards learning the lexical content of the password than the customer's voice characteristics. Therefore, a probabilistic framework that combines the hybrid HMM/MLP systems and GMM is proposed and extensively investigated. In this case, the HMM/MLP system is used for utterance verification, while GMM is used for speaker verification. Since UCP-SV involves both speech recognition (ASR) and speaker verification (SV), a natural extension of our work was to also investigate new approaches towards using ASR together with Speaker Recognition (SR) to improve both ASR and SR systems. In this framework, we show in this thesis that optimization and recognition based on a joint ASR-SR posterior probability criterion yields better ASR and SR performance, beyond what could be achieved from the two systems independently, as well as from a "sequential" approach (e.g., first performing speaker identification/ clustering, followed by speech recognition). This work resulted in a PC-based real time implementation of an HMM based UCP-SV system available for demonstration.

EPFL2005

Joint Speech and Speaker Recognition

The goal of the present thesis was to investigate and optimize different approaches towards User-Customized Password Speaker Verification (UCP-SV) systems. In such systems, users can choose their own passwords, which will be subsequently used for verification. The main assumption here unlike text-dependent speaker verification, is that no a priori knowledges about the password (such as its phonetic transcription) is available to the system. Speaker verification has already been widely investigated. However, although more user-friendly, UCP-SV is less understood and actually exhibits several new challenges, including: automatic inference of HMM password models (from a speaker-indepenent ASR system), fast speaker adaptation of the resulting acoustic models, score normalization, and verification (involving both text verification, i.e. ASR, and speaker verification). Evaluation of such as system is then based on its ability to simultaneously verify two hypotheses: (1) the identity of a claimed speaker, (2) pronouncing the correct password, and thus rejecting all other possible alternatives. In this thesis, two different speaker acoustic modeling approaches are investigated: HMM/GMM approach and hybrid HMM/MLP approach. Within the HMM/GMM approach, the background model used for likelihood normalization was particularly a difficult problem, and several solutions had to be investigated to improve the baseline system and to make the UCP-SV system actually practical. Within HMM/MLP approach, MLP adaptation was the main problem. We found that the modeling capabilities of the adapted MLP was more towards learning the lexical content of the password than the customer characteristics. Therefore, a probabilistic framework that combines the hybrid HMM/MLP systems and Gaussian Mixtures Models (GMM) is proposed. In this framework, the HMM/MLP system is used for utterance verification, while GMM is used for speaker verification. Experimental results showed comparable performance with HMM/GMM approach. Since UCP-SV involves both speech recognition (ASR) and speaker verification (SV), a natural extension of our work was to also investigate new approaches towards using ASR together with SV to improve both ASR and SV systems. In this thesis, we have shown that optimization and recognition based on a joint ASR-SV posterior probability was yielding better ASR and SV performance, beyond what could be achieved through a "sequential" approach (e.g., first performing speaker ID/clustering, followed by speech recognition).

IDIAP2005

Joint Speech and Speaker Recognition

École Polytechnique Fédérale de Lausanne, Computer Science Department2005

Joint speech and speaker recognition

EPFL2005

Joint Speech and Speaker Recognition

IDIAP2005

Joint Speech and Speaker Recognition

École Polytechnique Fédérale de Lausanne, Computer Science Department2005