Jason W. Pelecanos

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Speech contains valuable information regarding the traits of speakers. This paper investigates two aspects of this information. The first is automatic detection of non-native speakers and their native language on relatively large data sets. We present several experiments which show how our system outperforms the best published results on both the Fisher(More)
The effect of utterance length on the estimation of the likelihood of a speaker has previously seen a brief treatment in past works. In many speaker recognition evaluations, the utterances were typically configured to have a relatively consistent length. This paper investigates the effect of varying enrollment and test utterance lengths on the score(More)
A low-resource, text-independent speaker verification (SV) system with an efficient voice model compression technique is described, including its implementation exclusively with integer arithmetic. We describe and discuss the individual algorithmic steps, the integer implementation issues and its error analysis. Performance of the system is evaluated on(More)
Voice biometrics for user authentication is a task in which the object is to perform convenient, robust and secure authentication of speakers. In this work we investigate the use of state-of-the-art text-independent and text-dependent speaker verification technology for user authentication. We evaluate four different authentication conditions: speaker(More)
Bottleneck neural networks have recently found success in a variety of speech recognition tasks. This paper presents an approach in which they are utilized in the front-end of a speaker recognition system. The network inputs are melfrequency cepstral coefficients (MFCCs) from multiple consecutive frames and the outputs are speaker labels. We propose using a(More)
Universal background models (UBM) in speaker recognition systems are typically Gaussian mixture models (GMM) trained from a large amount of data using the maximum likelihood criterion. This paper investigates three alternative criteria for training the UBM. In the first, we cluster an existing automatic speech recognition (ASR) acoustic model to generate(More)
In this letter, we propose a discriminative modeling approach for the speaker verification problem that uses polynomial kernel support vector machines (PK-SVMs). The proposed approach is rooted in an equivalence relationship between the state-of-the-art probabilistic linear discriminant analysis (PLDA) and second degree polynomial kernel methods. We present(More)
Gaussian mixture models (QMM) have become one of the standard acoustic approaches for Language Detection. These models are typically incorporated to produce a log-likelihood ratio (LLR) verification statistic. In this framework, the intersession variability within each language becomes an adverse factor degrading the accuracy. To address this problem, we(More)