206 research outputs found

    A survey on modern trainable activation functions

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    In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest of the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as "trainable", "learnable" or "adaptable" activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constraints the corresponding weight layers.Comment: Published in "Neural Networks" journal (Elsevier

    Bio-motivated features and deep learning for robust speech recognition

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    Mención Internacional en el título de doctorIn spite of the enormous leap forward that the Automatic Speech Recognition (ASR) technologies has experienced over the last five years their performance under hard environmental condition is still far from that of humans preventing their adoption in several real applications. In this thesis the challenge of robustness of modern automatic speech recognition systems is addressed following two main research lines. The first one focuses on modeling the human auditory system to improve the robustness of the feature extraction stage yielding to novel auditory motivated features. Two main contributions are produced. On the one hand, a model of the masking behaviour of the Human Auditory System (HAS) is introduced, based on the non-linear filtering of a speech spectro-temporal representation applied simultaneously to both frequency and time domains. This filtering is accomplished by using image processing techniques, in particular mathematical morphology operations with an specifically designed Structuring Element (SE) that closely resembles the masking phenomena that take place in the cochlea. On the other hand, the temporal patterns of auditory-nerve firings are modeled. Most conventional acoustic features are based on short-time energy per frequency band discarding the information contained in the temporal patterns. Our contribution is the design of several types of feature extraction schemes based on the synchrony effect of auditory-nerve activity, showing that the modeling of this effect can indeed improve speech recognition accuracy in the presence of additive noise. Both models are further integrated into the well known Power Normalized Cepstral Coefficients (PNCC). The second research line addresses the problem of robustness in noisy environments by means of the use of Deep Neural Networks (DNNs)-based acoustic modeling and, in particular, of Convolutional Neural Networks (CNNs) architectures. A deep residual network scheme is proposed and adapted for our purposes, allowing Residual Networks (ResNets), originally intended for image processing tasks, to be used in speech recognition where the network input is small in comparison with usual image dimensions. We have observed that ResNets on their own already enhance the robustness of the whole system against noisy conditions. Moreover, our experiments demonstrate that their combination with the auditory motivated features devised in this thesis provide significant improvements in recognition accuracy in comparison to other state-of-the-art CNN-based ASR systems under mismatched conditions, while maintaining the performance in matched scenarios. The proposed methods have been thoroughly tested and compared with other state-of-the-art proposals for a variety of datasets and conditions. The obtained results prove that our methods outperform other state-of-the-art approaches and reveal that they are suitable for practical applications, specially where the operating conditions are unknown.El objetivo de esta tesis se centra en proponer soluciones al problema del reconocimiento de habla robusto; por ello, se han llevado a cabo dos líneas de investigación. En la primera líınea se han propuesto esquemas de extracción de características novedosos, basados en el modelado del comportamiento del sistema auditivo humano, modelando especialmente los fenómenos de enmascaramiento y sincronía. En la segunda, se propone mejorar las tasas de reconocimiento mediante el uso de técnicas de aprendizaje profundo, en conjunto con las características propuestas. Los métodos propuestos tienen como principal objetivo, mejorar la precisión del sistema de reconocimiento cuando las condiciones de operación no son conocidas, aunque el caso contrario también ha sido abordado. En concreto, nuestras principales propuestas son los siguientes: Simular el sistema auditivo humano con el objetivo de mejorar la tasa de reconocimiento en condiciones difíciles, principalmente en situaciones de alto ruido, proponiendo esquemas de extracción de características novedosos. Siguiendo esta dirección, nuestras principales propuestas se detallan a continuación: • Modelar el comportamiento de enmascaramiento del sistema auditivo humano, usando técnicas del procesado de imagen sobre el espectro, en concreto, llevando a cabo el diseño de un filtro morfológico que captura este efecto. • Modelar el efecto de la sincroní que tiene lugar en el nervio auditivo. • La integración de ambos modelos en los conocidos Power Normalized Cepstral Coefficients (PNCC). La aplicación de técnicas de aprendizaje profundo con el objetivo de hacer el sistema más robusto frente al ruido, en particular con el uso de redes neuronales convolucionales profundas, como pueden ser las redes residuales. Por último, la aplicación de las características propuestas en combinación con las redes neuronales profundas, con el objetivo principal de obtener mejoras significativas, cuando las condiciones de entrenamiento y test no coinciden.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Javier Ferreiros López.- Secretario: Fernando Díaz de María.- Vocal: Rubén Solera Ureñ

    Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

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    Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.Comment: submitted to ICASSP 2015 which does not perform blind review
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