12 accepted papers
The rapid progress in personalized speech generation technology, including personalized text-to-speech (TTS) and voice conversion (VC), poses a challenge in distinguishing between generated and real speech for human listeners, resulting in an urgent demand in protecting speakers' voices from malicio…
This work tackles the problem of the domestic audio tagging or environmental sound classification, where one audio recording can contain one or more acoustic events and a recognizer should output all of those tags. A baseline model for this task is a convolutional recurrent neural network (CRNN) wit…
In this paper, we study the role of long-time analytic phase of speech signals in spoken language recognition (SLR) and employ a set of features termed as instantaneous frequency cepstral coefficients (IFCC). We extract IFCC from long-time analytic phase, in an effort to capture long range acoustic…
Phonetic variability is one of the primary challenges in short duration speaker verification. This paper proposes a novel method that modifies the standard normal distribution prior in the total variability model to use a mixture of Gaussians as the prior distribution. The proposed speaker-phonetic…
Probabilistic linear discriminant analysis (PLDA) is widely described as an effective model for text-independent speaker verification in the i-vector space. The PLDA scoring function is typically formulated as the likelihood ratio between the speaker-adapted and the universal PLDAs. In this case, th…
This paper describes a new database for the assessment of automatic speaker verification (ASV) vulnerabilities to spoofing attacks. In contrast to other recent data collection efforts, the new database has been designed to support the development of replay spoofing countermeasures tailored towards t…
SIDEKIT is a new open-source Python toolkit that includes a large panel of state-of-the-art components and allow a rapid prototyping of an end-to-end speaker recognition system. For each step from front-end feature extraction, normalization, speech activity detection, modelling, scoring and visualiz…
I-vector has shown to be very effective in speaker verification with long-duration speech utterances. But when test utterances are of short duration, content mismatch between the enrollment and test utterances limit the performance of i-vector system. This paper proposes to extract local session var…
This paper presents a new approach and the study of GMM-SVM system for text-dependent speaker recognition on scenario of the fixed pass-phrases. The uniform-split content-based GMM-SVM system is proposed and applied to text-dependent speaker evaluation. We conducted detailed study of the proposed me…
Probabilistic linear discriminant analysis (PLDA) has shown to be effective for modeling channel variability in the i-vector space for text-independent speaker verification. Speaker verification is a binary hypothesis testing. Given a test segment, the verification score could be computed as the log…
Gaussian PLDA with uncertainty propagation is effective for i-vector based speaker verification. The idea is to propagate the uncertainty of i-vectors caused by the duration variability of utterances to the PLDA model. However, a limitation of the method is the difficulty of performing length normal…
An i-vector is a low-dimensional fixed-length representation of a variable-length speech utterance, and is defined as the posterior mean of a latent variable conditioned on the observed feature sequence of an utterance. The assumption is that the prior for the latent variable is non-informative, sin…