This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.

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Deep Learning: Methods and Applications

https://bura.brunel.ac.uk/bitstream/2438/14221/1/FullText.pdf

A survey of deep neural network architectures and their applications

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

Recent Advances in Convolutional Neural Networks

Deep learning in spiking neural networks

In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.

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A tutorial survey of architectures, algorithms, and applications for deep learning

This paper proposes a novel approach to voice conversion with non-parallel training data. The idea is to bridge between speakers by means of Phonetic PosteriorGrams (PPGs) obtained from a speaker-independent automatic speech recognition (SI-ASR) system. It is assumed that these PPGs can represent articulation of speech sounds in a speaker-normalized space and correspond to spoken content speaker-independently. The proposed approach first obtains PPGs of target speech. Then, a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Network (DBLSTM) structure is used to model the relationships between the PPGs and acoustic features of the target speech. To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments show that our approach performs equally well or better than state-of-the-art systems in both speech quality and speaker similarity.

Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

This paper investigates the use of Deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks (DBLSTM-RNNs) for voice conversion. Temporal correlations across speech frames are not directly modeled in frame-based methods using conventional Deep Neural Networks (DNNs), which results in a limited quality of the converted speech. To improve the naturalness and continuity of the speech output in voice conversion, we propose a sequence-based conversion method using DBLSTM-RNNs to model not only the frame-wised relationship between the source and the target voice, but also the long-range context-dependencies in the acoustic trajectory. Experiments show that DBLSTM-RNNs outperform DNNs where Mean Opinion Scores are 3.2 and 2.3 respectively. Also, DBLSTM-RNNs without dynamic features have better performance than DNNs with dynamic features.

/pdf/voice-conversion-using-deep-bidirectional-long-short-term-2cttcabgav.pdf

Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks

Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inputs and the acoustic features. Inspired by the intrinsically hierarchical process of human speech production and by the successful application of deep neural networks (DNNs) to automatic speech recognition (ASR), deep learning techniques have also been applied successfully to speech generation, as reported in recent literature.

Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inputs and the acoustic features. Inspired by the intrinsically hierarchical process of human speech production and by the successful application of deep neural networks (DNNs) to automatic speech recognition (ASR), deep learning techniques have also been applied successfully to speech generation, as reported in recent literature. This article systematically reviews these emerging speech generation approaches, with the dual goal of helping readers gain a better understanding of the existing techniques as well as stimulating new work in the burgeoning area of deep learning for parametric speech generation. In speech signal and information processing, many applications have been formulated as machine-learning tasks. ASR is a typical classification task that predicts word sequences from speech waveforms or feature sequences. There are also many regression tasks in speech processing that are aimed to generate speech signals from various types of inputs. They are referred to as speech generation tasks in this article. Speech generation covers a wide range of research topics in speech processing, such as text-to-speech (TTS) synthesis (generating speech from text), voice conversion (modifying nonlinguistic information of the input speech), speech enhancement (improving speech quality by noise reduction or other processing), and articulatory-to-acoustic mapping (converting articulatory movements to acoustic features). These

Deep Learning for Acoustic Modeling in Parametric Speech Generation

Deep belief network (DBN) has been shown to be a good generative model in tasks such as hand-written digit image generation. Previous work on DBN in the speech community mainly focuses on using the generatively pre-trained DBN to initialize a discriminative model for better acoustic modeling in speech recognition (SR). To fully utilize its generative nature, we propose to model the speech parameters including spectrum and F0 simultaneously and generate these parameters from DBN for speech synthesis. Compared with the predominant HMM-based approach, objective evaluation shows that the spectrum generated from DBN has less distortion. Subjective results also confirm the advantage of the spectrum from DBN, and the overall quality is comparable to that of context-independent HMM.

/pdf/multi-distribution-deep-belief-network-for-speech-synthesis-2va00z9w3h.pdf

Shiyin Kang

Papers

Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks

Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

Deep Learning for Acoustic Modeling in Parametric Speech Generation

Multi-distribution deep belief network for speech synthesis