Biometric Recognition Using Deep Learning: A Survey <s> SITW: <s> Abstract Automatic speech recognition by computers can provide the most natural and efficient method of communication between humans and computers. While in recent years high performance speech recognition systems are beginning to emerge from research institutions, scientists unequivocally agree that the deployment of speech recognition systems into realistic operating environments will require many hours of speech data to help us model the inherent variability in the speech signal. This paper describes the experiences of researchers at MIT in the collection of two large speech databases which have somewhat complementary objectives. The timit database was designed to be task and speaker-independent, and is suitable for general acoustic-phonetic research. The voyager database, on the other hand, was intended for development and evaluation of a system which incorporates both speech and natural language processing. This database is particularly valuable as a source of spontaneous utterances elicited in a realistic goal-oriented environment. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> SITW: <s> This paper describes, within the context of the DARPA EARS program, the design and implementation of the Fisher protocol for collecting conversational telephone speech which has yielded more than 16,000 English conversations. It also discusses the Quick Transcription specification that allowed 2000 hours of Fisher audio to be transcribed in less than one year. Fisher data is already in use within the DARPA EARS programs and will be published via the Linguistic Data Consortium for general use beginning in 2004. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> SITW: <s> This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> SITW: <s> Most existing datasets for speaker identification contain samples obtained under quite constrained conditions, and are usually hand-annotated, hence limited in size. The goal of this paper is to generate a large scale text-independent speaker identification dataset collected 'in the wild'. We make two contributions. First, we propose a fully automated pipeline based on computer vision techniques to create the dataset from open-source media. Our pipeline involves obtaining videos from YouTube; performing active speaker verification using a two-stream synchronization Convolutional Neural Network (CNN), and confirming the identity of the speaker using CNN based facial recognition. We use this pipeline to curate VoxCeleb which contains hundreds of thousands of 'real world' utterances for over 1,000 celebrities. Our second contribution is to apply and compare various state of the art speaker identification techniques on our dataset to establish baseline performance. We show that a CNN based architecture obtains the best performance for both identification and verification. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> SITW: <s> The objective of this paper is speaker recognition under noisy and unconstrained conditions. ::: We make two key contributions. First, we introduce a very large-scale audio-visual speaker recognition dataset collected from open-source media. Using a fully automated pipeline, we curate VoxCeleb2 which contains over a million utterances from over 6,000 speakers. This is several times larger than any publicly available speaker recognition dataset. ::: Second, we develop and compare Convolutional Neural Network (CNN) models and training strategies that can effectively recognise identities from voice under various conditions. The models trained on the VoxCeleb2 dataset surpass the performance of previous works on a benchmark dataset by a significant margin. <s> BIB005 </s> Biometric Recognition Using Deep Learning: A Survey <s> SITW: <s> Learning good representations is of crucial importance in deep learning. Mutual Information (MI) or similar measures of statistical dependence are promising tools for learning these representations in an unsupervised way. Even though the mutual information between two random variables is hard to measure directly in high dimensional spaces, some recent studies have shown that an implicit optimization of MI can be achieved with an encoder-discriminator architecture similar to that of Generative Adversarial Networks (GANs). In this work, we learn representations that capture speaker identities by maximizing the mutual information between the encoded representations of chunks of speech randomly sampled from the same sentence. The proposed encoder relies on the SincNet architecture and transforms raw speech waveform into a compact feature vector. The discriminator is fed by either positive samples (of the joint distribution of encoded chunks) or negative samples (from the product of the marginals) and is trained to separate them. We report experiments showing that this approach effectively learns useful speaker representations, leading to promising results on speaker identification and verification tasks. Our experiments consider both unsupervised and semi-supervised settings and compare the performance achieved with different objective functions. <s> BIB006
The Speakers in the Wild (SITW) dataset was acquired across unconstrained conditions  . Unlike the SRE datasets, this data was not collected under controlled conditions and thus contains real noise and reverberation. The database consists of recordings of 299 speakers, with an average of eight different sessions per person. VoxCeleb: The VoxCeleb dataset BIB004 and Vox-Celeb2 dataset BIB005 are public datasets compiled from interview videos uploaded to YouTube to emphasize the lack of large scale unconstrained data for speaker recognition. These data are collected using a fully automated pipeline. A two-stream synchronization CNN is used to estimate the correlation between the audio track and the mouth motion of the video, and then CNN-based facial recognition techniques are used to identify speakers for speech annotation. VoxCeleb1 contains over 100,000 utterances for 1,251 celebrities, and VoxCeleb2 contains over a million utterances for 6,112 identities. Apart from datasets designed purely for speaker recognition tasks, many datasets collected for automatic speech recognition can also be used for training or evaluation of speaker recognition systems. For example, the Switchboard dataset  and the Fisher Corpus BIB002 , which were originally collected for speech recognition tasks, are also used for model training in NIST Speaker Recognition Evaluations. On the other hand, researchers may utilize existing speech recognition datasets to prepare their own speaker recognition evaluation dataset to prove the effectiveness of their research. For example, Librispeech dataset BIB003 and the TIMIT dataset BIB001 are pre-processed by the author in BIB006 to serve as evaluation set for speaker recognition task.