Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

Spoof detection using time-delay shallow neural network and feature switching

Detecting spoofed utterances is a fundamental problem in voice-based biometrics. Spoofing can be performed either by logical accesses like speech synthesis, voice conversion or by physical accesses such as replaying the pre-recorded utterance. Inspired by the state-of-the-art \emph{x}-vector based speaker verification approach, this paper proposes a time-delay shallow neural network (TD-SNN) for spoof detection for both logical and physical access. The novelty of the proposed TD-SNN system vis-a-vis conventional DNN systems is that it can handle variable length utterances during testing. Performance of the proposed TD-SNN systems and the baseline Gaussian mixture models (GMMs) is analyzed on the ASV-spoof-2019 dataset. The performance of the systems is measured in terms of the minimum normalized tandem detection cost function (min-t-DCF). When studied with individual features, the TD-SNN system consistently outperforms the GMM system for physical access. For logical access, GMM surpasses TD-SNN systems for certain individual features. When combined with the decision-level feature switching (DLFS) paradigm, the best TD-SNN system outperforms the best baseline GMM system on evaluation data with a relative improvement of 48.03\% and 49.47\% for both logical and physical access, respectively

Glottal Source Cepstrum Coefficients Applied to NIST SRE 2010

Through the present paper, a novel feature set for speaker recognition based on glottal estimate information is presented. An iterative algorithm is used to derive the vocal tract and glottal source estimations from speech signal. In order to test the importance of glottal source information in speaker characterization, the novel feature set has been tested in the 2010 NIST Speaker Recognition Evaluation (NIST SRE10). The proposed system uses glottal estimate parameter templates and classical cepstral information to build a model for each speaker involved in the recognition process. ALIZE [1] open-source software has been used to create the GMM models for both background and target speakers. Compared to using mel-frequency cepstrum coefficients (MFCC), the misclassification rate for the NIST SRE 2010 reduced from 29.43% to 27.15% when glottal source features are use

Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings

In forensic voice comparison the speaker embedding has become widely popular in the last 10 years. Most of the pretrained speaker embeddings are trained on English corpora, because it is easily accessible. Thus, language dependency can be an important factor in automatic forensic voice comparison, especially when the target language is linguistically very different. There are numerous commercial systems available, but their models are mainly trained on a different language (mostly English) than the target language. In the case of a low-resource language, developing a corpus for forensic purposes containing enough speakers to train deep learning models is costly. This study aims to investigate whether a model pre-trained on English corpus can be used on a target low-resource language (here, Hungarian), different from the model is trained on. Also, often multiple samples are not available from the offender (unknown speaker). Therefore, samples are compared pairwise with and without speaker enrollment for suspect (known) speakers. Two corpora are applied that were developed especially for forensic purposes, and a third that is meant for traditional speaker verification. Two deep learning based speaker embedding vector extraction methods are used: the x-vector and ECAPA-TDNN. Speaker verification was evaluated in the likelihood-ratio framework. A comparison is made between the language combinations (modeling, LR calibration, evaluation). The results were evaluated by minCllr and EER metrics. It was found that the model pre-trained on a different language but on a corpus with a huge amount of speakers performs well on samples with language mismatch. The effect of sample durations and speaking styles were also examined. It was found that the longer the duration of the sample in question the better the performance is. Also, there is no real difference if various speaking styles are applied

Alcohol Language Corpus

The Alcohol Language Corpus (ALC) is the first publicly available speech corpus comprising intoxicated and sober speech of 162 female and male German speakers. Recordings are done in the automotive environment to allow for the development of automatic alcohol detection and to ensure a consistent acoustic environment for the alcoholized and the sober recording. The recorded speech covers a variety of contents and speech styles. Breath and blood alcohol concentration measurements are provided for all speakers. A transcription according to SpeechDat/Verbmobil standards and disfluency tagging as well as an automatic phonetic segmentation are part of the corpus. An Emu version of ALC allows easy access to basic speech parameters as well as the us of R for statistical analysis of selected parts of ALC. ALC is available without restriction for scientific or commercial use at the Bavarian Archive for Speech Signals