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Stochastic Fusion for Multi-stream Neural Network in Video Classification
(2019-01-01)
Huang, Yu-Min
;
Tseng, Huan-Hsin
;
Chien, Jen-Tzung
;
電機工程學系
;
Department of Electrical and Computer Engineering
Spatial image and optical how provide complementary information for video representation and classification. Traditional methods separately encode two stream signals and then fuse them at the end of streams. This paper presents a new multi-stream recurrent neural network where streams are tightly coupled at each time step. Importantly, we propose a stochastic fusion mechanism for multiple streams of video data based on the Gumbel samples to increase the prediction power. A stochastic backpropagation algorithm is implemented to carry out a multi-stream neural network with stochastic fusion based on a joint optimization of convolutional encoder and recurrent decoder. Experiments on UCF101 dalaset illustrate the merits of the proposed stochastic fusion in recurrent neural network in terms of interpretation and classification performance.
Guest Editorial: Modern Speech Processing and Learning
(1970-01-01)
Chien, Jen-Tzung
;
Mak, Man-Wai
;
電機工程學系
;
Department of Electrical and Computer Engineering
Variational Domain Adversarial Learning With Mutual Information Maximization for Speaker Verification
(2020-01-01)
Tu, Youzhi
;
Mak, Man-Wai
;
Chien, Jen-Tzung
;
電機工程學系
;
Department of Electrical and Computer Engineering
Domain mismatch is a common problem in speaker verification (SV) and often causes performance degradation. For the system relying on the Gaussian PLDA backend to suppress the channel variability, the performance would be further limited if there is no Gaussianity constraint on the learned embeddings. This paper proposes an information-maximized variational domain adversarial neural network (InfoVDANN) that incorporates an InfoVAE into domain adversarial training (DAT) to reduce domain mismatch and simultaneously meet the Gaussianity requirement of the PLDA backend. Specifically, DAT is applied to produce speaker discriminative and domain-invariant features, while the InfoVAE performs variational regularization on the embedded features so that they follow a Gaussian distribution. Another benefit of the InfoVAE is that it avoids posterior collapse in VAEs by preserving the mutual information between the embedded features and the training set so that extra speaker information can be retained in the features. Experiments on both SRE16 and SRE18-CMN2 show that the InfoVDANN outperforms the recent VDANN, which suggests that increasing the mutual information between the embedded features and input features enables the InfoVDANN to extract extra speaker information that is otherwise not possible.
Guest Editorial: Modern Speech Processing and Learning
(2020-08-01)
Chien, Jen-Tzung
;
Mak, Man-Wai
;
電機工程學系
;
Department of Electrical and Computer Engineering
Deep Bayesian Data Mining
(2020-01-01)
Chien, Jen-Tzung
;
交大名義發表
;
National Chiao Tung University
This tutorial addresses the fundamentals and advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition [7, 55] to document summarization [8], text classification [5, 75], text segmentation [18], information extraction [50], image caption generation [69, 72], sentence generation [25, 46], dialogue control [22, 76], sentiment classification, recommendation system, question answering [58] and machine translation [2], to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process [61], Chinese restaurant process [4], hierarchical Pitman-Yor process [60], Indian buffet process [35], recurrent neural network (RNN) [26, 41, 48, 65], long short-term memory, sequence-to-sequence model [59], variational auto-encoder (VAE) [44], generative adversarial network (GAN) [36], attention mechanism [27, 56], memory-augmented neural network [39, 58], skip neural network [6], temporal difference VAE [40], stochastic neural network [3, 47], stochastic temporal convolutional network [1], predictive state neural network [31], and policy neural network [49, 74]. Enhancing the prior/posterior representation is addressed [53, 62]. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models [54]. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies, tasks and applications are presented to tackle different issues in deep Bayesian mining, searching, learning and understanding. At last, we will point out a number of directions and outlooks for future studies. This tutorial serves the objectives to introduce novices to major topics within deep Bayesian learning, motivate and explain a topic of emerging importance for data mining and natural language understanding, and present a novel synthesis combining distinct lines of machine learning work.
MARKOV RECURRENT NEURAL NETWORK LANGUAGE MODEL
(2019-01-01)
Chien, Jen-Tzung
;
Kuo, Che-Yu
;
電機工程學系
;
Department of Electrical and Computer Engineering
Recurrent neural network (RNN) has achieved a great success in language modeling where the temporal information based on deterministic state is continuously extracted and evolved through time. Such a simple deterministic transition function using input-to-hidden and hidden-to-hidden weights is usually insufficient to reflect the diversities and variations of latent variable structure behind the heterogeneous natural language. This paper presents a new stochastic Markov RNN (MRNN) to strengthen the learning capability in language model where the trajectory of word sequences is driven by a neural Markov process with Markov state transitions based on a K-state long short-term memory model. A latent state machine is constructed to characterize the complicated semantics in the structured lexical patterns. Gumbel-softmax is introduced to implement the stochastic backpropatation algorithm with discrete states. The parallel computation for rapid realization of MRNN is presented. The variational Bayesian learning procedure is implemented. Experiments demonstrate the merits of stochastic and diverse representation using MRNN language model where the overhead of parameters and computations is limited.
BAYESIAN ADVERSARIAL LEARNING FOR SPEAKER RECOGNITION
(2019-01-01)
Chien, Jen-Tzung
;
Kuo, Chun-Lin
;
電機工程學系
;
Department of Electrical and Computer Engineering
This paper presents a new generative adversarial network (GAN) which artificially generates the i-vectors to compensate the imbalanced or insufficient data in speaker recognition based on the probabilistic linear discriminant analysis. Theoretically, GAN is powerful to generate the artificial data which are misclassified as the real data. However, GAN suffers from the mode collapse problem in two-player optimization over generator and discriminator. This study deals with this challenge by improving the model regularization through characterizing the weight uncertainty in GAN. A new Bayesian GAN is implemented to learn a regularized model from diverse data where the strong modes are flattened via the marginalization. In particular, we present a variational GAN (VGAN) where the encoder, generator and discriminator are jointly estimated according to the variational inference. The computation cost is significantly reduced. To assure the preservation of gradient values, the learning objective based on Wasserstein distance is further introduced. The issues of model collapse and gradient vanishing are alleviated. Experiments on NIST i-vector Speaker Recognition Challenge demonstrate the superiority of the proposed VGAN to the variational autoencoder, the standard GAN and the Bayesian GAN based on the sampling method. The learning efficiency and generation performance are evaluated.
The Role of Accent and Grouping Structures in Estimating Musical Meter
(2020-04-01)
Lin, Han-Ying
;
Huang, Chien-Chieh
;
Chang, Wen-Whei
;
Chien, Jen-Tzung
;
電機工程學系
This study presents a new method to exploit both accent and grouping structures of music in meter estimation. The system starts by extracting autocorrelation-based features that characterize accent periodicities. Based on the local boundary detection model, we construct grouping features that serve as additional cues for inferring meter. After the feature extraction, a multi-layer cascaded classifier based on neural network is incorporated to derive the most likely meter of input melody. Experiments on 7351 folk melodies in MIDI files indicate that the proposed system achieves an accuracy of 95.76% for classification into nine categories of meters.
Deep Bayesian Mining, Learning and Understanding
(2019-01-01)
Chien, Jen-Tzung
;
交大名義發表
;
National Chiao Tung University
This tutorial addresses the advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network (RNN), long short-term memory, sequence-to-sequence model, variational auto-encoder (VAE), generative adversarial network (GAN), attention mechanism, memory-augmented neural network, skip neural network, stochastic neural network, predictive state neural network, policy neural network. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies are presented to tackle different issues in deep Bayesian mining, learning and understanding. At last, we will point out a number of directions and outlooks for future studies.
STOCHASTIC MARKOV RECURRENT NEURAL NETWORK FOR SOURCE SEPARATION
(2019-01-01)
Chien, Jen-Tzung
;
Kuo, Che-Yu
;
電機工程學系
;
Department of Electrical and Computer Engineering
Monaural source separation based on recurrent neural network is learned to characterize the sequential patterns in source signals based on dynamic states which are propagated through time. The hidden states are assumed to be deterministic along a single path where a shared long short-term memory (LSTM) is used. Such assumptions may not faithfully reflect the randomness and the variety of temporal features in mixed signals. To strengthen the capability of LSTM in source separation, we propose a stochastic Markov LSTM where the regression from the mixed signal to its source signals is learned with a stochastic indicator of Markov state which selects the state-dependent LSTM for signal separation at each time. A set of LSTMs is discovered to capture the structural diversity of temporal signals or the stochastic trajectory of state transitions for sequential prediction. A new state machine is constructed to learn the complicated latent semantics in heterogeneous and structural mappings between mixed signals and source signals. The Gumbel-softmax sampling is implemented in the backpropagation algorithm with discrete Markov states. Experiments on speech enhancement illustrate the merit of the proposed stochastic Markov LSTM in terms of short-term objective intelligibility measure of the separated speech.

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