Adversarial Network Bottleneck Features for Noise Robust Speaker Verification

In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an encoding network (EN) and a discriminative network (DN). Mel-frequency cepstral coefficients (MFCCs) of clean and noisy speech are used as input to the EN and the output of the EN is used as the noise robust feature. The EN and DN are trained in turn, namely, when training the DN, noise types are selected as the training labels and when training the EN, all labels are set as the same, i.e., the clean speech label, which aims to make the AN features invariant to noise and thus achieve noise robustness. We evaluate the performance of the proposed feature on a Gaussian Mixture Model-Universal Background Model based speaker verification system, and make comparison to MFCC features of speech enhanced by short-time spectral amplitude minimum mean square error (STSA-MMSE) and deep neural network-based speech enhancement (DNN-SE) methods. Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios. Furthermore, the AN-BN feature is able to improve the speaker verification performance under the clean condition

Text-independent speaker recognition

This research presents new text-independent speaker recognition system with multivariate tools such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) embedded into the recognition system after the feature extraction step. The proposed approach evaluates the performance of such a recognition system when trained and used in clean and noisy environments. Additive white Gaussian noise and convolutive noise are added. Experiments were carried out to investigate the robust ability of PCA and ICA using the designed approach. The application of ICA improved the performance of the speaker recognition model when compared to PCA. Experimental results show that use of ICA enabled extraction of higher order statistics thereby capturing speaker dependent statistical cues in a text-independent recognition system. The results show that ICA has a better de-correlation and dimension reduction property than PCA. To simulate a multi environment system, we trained our model such that every time a new speech signal was read, it was contaminated with different types of noises and stored in the database. Results also show that ICA outperforms PCA under adverse environments. This is verified by computing recognition accuracy rates obtained when the designed system was tested for different train and test SNR conditions with additive white Gaussian noise and test delay conditions with echo effect

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

ROBUST SPEAKER RECOGNITION BASED ON LATENT VARIABLE MODELS

Automatic speaker recognition in uncontrolled environments is a very challenging task due to channel distortions, additive noise and reverberation. To address these issues, this thesis studies probabilistic latent variable models of short-term spectral information that leverage large amounts of data to achieve robustness in challenging conditions. Current speaker recognition systems represent an entire speech utterance as a single point in a high-dimensional space. This representation is known as "supervector". This thesis starts by analyzing the properties of this representation. A novel visualization procedure of supervectors is presented by which qualitative insight about the information being captured is obtained. We then propose the use of an overcomplete dictionary to explicitly decompose a supervector into a speaker-specific component and an undesired variability component. An algorithm to learn the dictionary from a large collection of data is discussed and analyzed. A subset of the entries of the dictionary is learned to represent speaker-specific information and another subset to represent distortions. After encoding the supervector as a linear combination of the dictionary entries, the undesired variability is removed by discarding the contribution of the distortion components. This paradigm is closely related to the previously proposed paradigm of Joint Factor Analysis modeling of supervectors. We establish a connection between the two approaches and show how our proposed method provides improvements in terms of computation and recognition accuracy. An alternative way to handle undesired variability in supervector representations is to first project them into a lower dimensional space and then to model them in the reduced subspace. This low-dimensional projection is known as "i-vector". Unfortunately, i-vectors exhibit non-Gaussian behavior, and direct statistical modeling requires the use of heavy-tailed distributions for optimal performance. These approaches lack closed-form solutions, and therefore are hard to analyze. Moreover, they do not scale well to large datasets. Instead of directly modeling i-vectors, we propose to first apply a non-linear transformation and then use a linear-Gaussian model. We present two alternative transformations and show experimentally that the transformed i-vectors can be optimally modeled by a simple linear-Gaussian model (factor analysis). We evaluate our method on a benchmark dataset with a large amount of channel variability and show that the results compare favorably against the competitors. Also, our approach has closed-form solutions and scales gracefully to large datasets. Finally, a multi-classifier architecture trained on a multicondition fashion is proposed to address the problem of speaker recognition in the presence of additive noise. A large number of experiments are conducted to analyze the proposed architecture and to obtain guidelines for optimal performance in noisy environments. Overall, it is shown that multicondition training of multi-classifier architectures not only produces great robustness in the anticipated conditions, but also generalizes well to unseen conditions

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others