Bio-motivated features and deep learning for robust speech recognition

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

Detección de eventos en secuencias con multitudes

El objetivo de este proyecto es la implementación de un sistema de reconocimiento automático de eventos anóomalos en secuencias de vídeo donde se vean involucradas un gran número de personas. Para desarrollar y probar el sistema se ha utilizado la base de datos PETS Dataset S3, High Level, que contiene siete secuencias de vídeo en las aparecen los siguientes eventos: - Walking: Representa a un n úmero significativo de personas desplaz andose lentamente. - Running: Representa a un número significativo de personas desplazándose rápidamente. - Evacuation: Representa la dispersión rápida en diferentes direcciones de una multitud. - Crowd Formation: Representa la unión en un grupo de un gran número de individuos provenientes de diferentes direcciones. - Crowd Splitting: Representa la división de un grupo de individuos, en dos o más grupos que toman diferentes direcciones. - Local Dispersion: Representa la dispersión de un pequeño grupo de individuos de una multitud.Ingeniería de Sistemas Audiovisuale

Mid-level feature set for specific event and anomaly detection in crowded scenes

Proceedings of: 20th IEEE International Conference on Image Processing (ICIP 2013). Melbourne, Australia, September 15-18, 2013.In this paper we propose a system for automatic detection of specific events and abnormal behaviors in crowded scenes. In particular, we focus on the parametrization by proposing a set of mid-level spatio-temporal features that successfully model the characteristic motion of typical events in crowd behaviors. Furthermore, due to the fact that some features are more suitable than others to model specific events of interest, we also present an automatic process for feature selection. Our experiments prove that the suggested feature set works successfully for both explicit event detection and distance-based anomaly detection tasks. The results on PETS for explicit event detection are generally better than those previously reported. Regarding anomaly detection, the proposed method performance is comparable to those of state-of-the-art method for PETS and substantially better than that reported for Web dataset.Publicad

Deep Maxout Networks applied to Noise-Robust Speech Recognition

Proceedings of: IberSPEECH 2014 "VIII Jornadas en Tecnologías del Habla" and "IV Iberian SLTech Workshop". Las Palmas de Gran Canaria, Spain, November 19-21, 2014.Deep Neural Networks (DNN) have become very popular for acoustic modeling due to the improvements found over traditional Gaussian Mixture Models (GMM). However, not many works have addressed the robustness of these systems under noisy conditions. Recently, the machine learning community has proposed new methods to improve the accuracy of DNNs by using techniques such as dropout and maxout. In this paper, we investigate Deep Maxout Networks (DMN) for acoustic modeling in a noisy automatic speech recognition environment. Experiments show that DMNs improve substantially the recognition accuracy over DNNs and other traditional techniques in both clean and noisy conditions on the TIMIT dataset.This contribution has been supported by an Airbus Defense and Space Grant (Open Innovation - SAVIER) and Spanish Government-CICYT project 2011-26807/TEC.Publicad

ASR Feature Extraction with Morphologically-Filtered Power-Normalized Cochleograms

Proceedings of: 15th Annual Conference of the International Speech Communication Association. Singapore, September 14-18, 2014.In this paper we present advances in the modeling of the masking behavior of the Human Auditory System to enhance the robustness of the feature extraction stage in Automatic Speech Recognition. The solution adopted is based on a non-linear filtering of a spectro-temporal representation applied simultaneously on both the frequency and time domains, by processing it using mathematical morphology operations as if it were an image. A particularly important component of this architecture is the so called structuring element: biologically-based considerations are addressed in the present contribution to design an element that closely resembles the masking phenomena taking place in the cochlea. The second feature of this contribution is the choice of underlying spectro-temporal representation. The best results were achieved by the representation introduced as part of the Power Normalized Cepstral Coefficients together with a spectral subtraction step. On the Aurora 2 noisy continuous digits task, we report relative error reductions of 18.7% compared to PNCC and 39.5% compared to MFCC.This contribution has been supported by an Airbus Defense and Space Grant (Open Innovation - SAVIER) and Spanish Government-CICYT project 2011-26807/TEC.Publicad

Morphologically filtered power-normalized cochleograms as robust, biologically inspired features for ASR

In this paper, we present advances in the modeling of the masking behavior of the human auditory system (HAS) to enhance the robustness of the feature extraction stage in automatic speech recognition (ASR). The solution adopted is based on a nonlinear filtering of a spectro-temporal representation applied simultaneously to both frequency and time domains-as if it were an image-using mathematical morphology operations. A particularly important component of this architecture is the so-called structuring element (SE) that in the present contribution is designed as a single three-dimensional pattern using physiological facts, in such a way that closely resembles the masking phenomena taking place in the cochlea. A proper choice of spectro-temporal representation lends validity to the model throughout the whole frequency spectrum and intensity spans assuming the variability of the masking properties of the HAS in these two domains. The best results were achieved with the representation introduced as part of the power normalized cepstral coefficients (PNCC) together with a spectral subtraction step. This method has been tested on Aurora 2, Wall Street Journal and ISOLET databases including both classical hidden Markov model (HMM) and hybrid artificial neural networks (ANN)-HMM back-ends. In these, the proposed front-end analysis provides substantial and significant improvements compared to baseline techniques: up to 39.5% relative improvement compared to MFCC, and 18.7% compared to PNCC in the Aurora 2 database.This contribution has been supported by an Airbus Defense and Space Grant (Open Innovation - SAVIER) and Spanish Government-CICYT projects TEC2014-53390-P and TEC2014-61729-EX