The output scores of most speaker recognition systems are not directly interpretable as stand-alone values. For this reason, a calibration step is usually performed on the scores to convert them into proper likelihood ratios (LR), which have a clear probabilistic interpretation. The standard calibration approach transforms the system scores using a linear function trained using data selected to closely match the evaluation conditions. This selection, though, is not feasible when the evaluation conditions are unknown. In previous work, we proposed a calibration approach for this scenario called trialbased calibration (TBC). TBC trains a separate calibration model for each test trial using data that is dynamically selected from a candidate training set to match the conditions of the trial. In this work, we extend the TBC method, proposing (1) a new similarity metric for selecting training data that results in significant gains over the one proposed in the original work, (2) a new option that enables the system to reject a trial when not enough matched data is available for training the calibration model, and (3) the use of regularization to improve the robustness of the calibration models trained for each trial. We test the proposed algorithms on a development set composed of several conditions and on the FBI multi-condition speaker recognition dataset, and we demonstrate that the proposed approach reduces calibration loss to values close to 0 for most conditions when matched calibration data is available for selection and that it can reject most trials for which relevant calibration data is unavailable.
Robust Speaker Recognition from Distant Speech under Real Reverberant Environments Using Speaker Embeddings
This article focuses on speaker recognition using speech acquired using a single distant or far-field microphone in an indoors environment. This study differs from the majority of speaker recognition research, which focuses on speech acquisition over short distances, such as when using a telephone handset or mobile device or far-field microphone arrays, for which beamforming can enhance distant speech signals. We use two large-scale corpora collected by retransmitting speech data in reverberant environments with multiple microphones placed at different distances. We first characterize three different speaker recognition systems ranging from a traditional universal background model (UBM) i-vector system to a state-of-the-art deep neural network (DNN) speaker embedding system with a probabilistic linear discriminant analysis (PLDA) back-end. We then assess the impact of microphone distance and placement, background noise, and loudspeaker orientation on the performance of speaker recognition system for distant speech data. We observe that the recently introduced DNN speaker embedding based systems are far more robust compared to i-vector based systems, providing a significant relative improvement of up to 54% over the baseline UBM i-vector system, and 45.5% over prior DNN-based speaker recognition technology.
Deep neural network (DNN)-based speaker embeddings have resulted in new, state-of-the-art text-independent speaker recognition technology. However, very limited effort has been made to understand DNN speaker embeddings. In this study, our aim is analyzing the behavior of the speaker recognition systems based on speaker embeddings toward different front-end features, including the standard Mel frequency cepstral coefficients (MFCC), as well as power normalized cepstral coefficients (PNCC), and perceptual linear prediction (PLP). Using a speaker recognition system based on DNN speaker embeddings and probabilistic linear discriminant analysis (PLDA), we compared different approaches to leveraging complementary information using score-, embeddings-, and feature-level combination. We report our results for Speakers in the Wild (SITW) and NIST SRE 2016 datasets. We found that first and second embeddings layers are complementary in nature. By applying score and embedding-level fusion we demonstrate relative improvements in equal error rate of 17% on NIST SRE 2016 and 10% on SITW over the baseline system.
This paper introduces the Voices Obscured in Complex Environmental Settings (VOiCES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions. Publicly available speech corpora are mostly composed of isolated speech at close-range microphony. A typical approach to better represent realistic scenarios, is to convolve clean speech with noise and simulated room response for model training. Despite these efforts, model performance degrades when tested against uncurated speech in natural conditions. For this corpus, audio was recorded in furnished rooms with background noise played in conjunction with foreground speech selected from the LibriSpeech corpus. Multiple sessions were recorded in each room to accommodate for all foreground speech-background noise combinations. Audio was recorded using twelve microphones placed throughout the room, resulting in 120 hours of audio per microphone. This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.
Analysis of Phonetic Markedness and Gestural Effort Measures for Acoustic Speech-Based Depression Classification
While acoustic-based links between clinical depression and abnormal speech have been established, there is still however little knowledge regarding what kinds of phonological content is most impacted. Moreover, for automatic speech-based depression classification and depression assessment elicitation protocols, even less is understood as to what phonemes or phoneme transitions provide the best analysis. In this paper we analyze articulatory measures to gain further insight into how articulation is affected by depression. In our investigative experiments, by partitioning acoustic speech data based on
lower to high densities of specific phonetic markedness and gestural effort, we demonstrate improvements in depressed/non-depressed classification accuracy and F1 scores.
To date, automatic spoken language detection research has largely been based on a closed-set paradigm, in which the languages to be detected are known prior to system application. In actual practice, such systems may face previously unseen languages (out-of-set (OOS) languages) which should be rejected, a common problem that has received limited attention from the research community. In this paper, we focus on situations in which either (1) the system-modeled languages are not observed during use or (2) the test data contains OOS languages that are unseen during modeling or calibration. In these situations, the common multi-class objective function for calibration of language-detection scores is problematic. We describe how the assumptions of multi-class calibration are not always fulfilled in a practical sense and explore applying global and language-dependent binary objective functions to relax system constraints. We contrast the benefits and sensitivities of the calibration approaches on practical scenarios by presenting results using both LRE09 data and 14 languages from the BABEL dataset. We show that the global binary approach is less sensitive to the characteristics of the training data and that OOS modeling with individual detectors is the best option when OOS test languages are not known to the system.
Unsupervised techniques for the adaptation of speaker recognition are important due to the problem of condition mismatch that is prevalent when applying speaker recognition technology to new conditions and the general scarcity of labeled ‘in-domain’ data. In the recent NIST 2016 Speaker Recognition Evaluation (SRE), symmetric score normalization (Snorm) and calibration using unlabeled in-domain data were shown to be beneficial. Because calibration requires speaker labels for training, speaker-clustering techniques were used to generate pseudo-speakers for learning calibration parameters in those cases where only unlabeled in-domain data was available. These methods performed well in the SRE16. It is unclear, however, whether those techniques generalize well to other data sources. In this work, we benchmark these approaches on several distinctly different databases, after we describe our SRICON-UAM team system submission for the NIST 2016 SRE. Our analysis shows that while the benefit of S-norm is also observed across other datasets, applying speaker-clustered calibration provides considerably greater benefit to the system in the context of new acoustic conditions.
The newly collected Speakers in the Wild (SITW) database was central to a text-independent speaker recognition challenge held as part of a special session at Interspeech 2016. The SITW database is composed of audio recordings from 299 speakers collected from open source media, with an average of 8 sessions per speaker. The recordings contain unconstrained or “wild” acoustic conditions, rarely found in large speaker recognition datasets, and multi-speaker recordings for both speaker enrollment and verification. This article provides details of the SITW speaker recognition challenge and analysis of evaluation results. There were 25 international teams involved in the challenge of which 11 teams participated in an evaluation track. Teams were tasked with applying existing and novel speaker recognition algorithms to the challenges associated with the real world conditions of SITW. We provide an analysis of some of the top performing systems submitted during the evaluation and provide future research directions.
The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology on single and multi-speaker audio acquired across unconstrained or “wild” conditions. The database consists of recordings of 299 speakers, with an average of eight different sessions per person. Unlike existing databases for speaker recognition, this data was not collected under controlled conditions and thus contains real noise, reverberation, intra-speaker variability and compression artifacts. These factors are often convolved in the real world, as the SITW data shows, and they make SITW a challenging database for single- and multi-speaker recognition.