Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Digital processing of speech signals

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Digital Coding of Waveforms

1. Spoken language input Ronald Cole, Victor Zue, Wayne Ward, Melvyn J. Hunt, Richard M. Stern, Renato De Mori, Fabio Brugnara, Salim Roukos, Sadaoki Furui and Patti Price 2. Written language input Joseph Mariani, Sargur N. Srihari, Rohini K. Srihari, Richard G. Casey, Abdel Belaid, Claudie Faure, Eric Lecolinet, Isabelle Guyo, Colin Warwick and Rejean Plamondon 3. Language analysis and understanding Annie Zaenen, Hans Uszkoreit, Fred Karlsson, Lauri Karttunen, Antonio Sanfilippo, Stephen F. Pulman, Fernando Pereira and Ted Briscoe 4. Language generation Hans Uszkoreit, Eduard Hovy, Gertjan van Noord, Gunter Neumann and John Bateman 5. Spoken output technologies Ronald Cole, Yoshinori Sagisaka, Christophe d'Alessandro, Jean-Sylvain Lienard, Richard Sproat, Kathleen R. McKeown and Johanna D. Moore 6. Discourse and dialogue Hans Uszkoreit, Barbara Grosz, Donia Scott, Hans Kamp, Phil Cohe and Egidio Giachin 7. Document processing Annie Zaenen, Per-Kristian Halvorsen, Donna Harman, Peter Schauble, Alan Smeaton, Paul Jacobs, Karen Sparck Jones, Robert Dale, Richard H. Wojcik and James E. Hoard 8. Multilinguality Annie Zaenen, Martin Kay, Christian Boitet, Christian Fluhr, Alexander Waibel, Yeshwant K. Muthusamy and A. Lawrence Spitz 9. Multimodality Joseph Mariani, James L. Flanagan, Gerard Ligozat, Wolfgang Wahlster, Yacine Bellik, Alan J. Goldschen, Christian Benoit, Dominic W. Massaro and Michael M. Cohen 10. Transmission and storage Victor Zue, Isabel Trancoso, Bishnu S. Atal, Nikil S. Jayant and Dirk Van Compernolle 11. Mathematical methods Ronald Cole, Hans Uszkoreit, Steve Levinson, John Makhoul, Aravind Joshi, Herve Bourlard, Nelson Morgan, Ronald M. Kaplan and John Bridle 12. Language resources Ronald Cole, Antonio Zampolli, Eva Ejerhed, Ken Church, Lori Lamel, Ralph Grishman, Nicoletta Calzolari, Christian Galinski and Gerhard Budin 13. Evaluation Joseph Mariani, Lynette Hirschman, Henry S. Thompson, Beth Sundheim, John Hutchins, Ezra Black, Margaret King, David S. Pallett, Adrian Fourcin, Louis C. W. Pols, Sharon Oviatt, Herman J. M. Steeneken and Junichi Kanai Glossary Citation index Index.

Survey of the State of the Art in Human Language Technology

The access to the Information Society for deaf and hard of hearing people is a major issue that must be addressed. Subtitling of broadcast news programs is a crucial application that must be incorporated by television companies in order to allow this accessibility and not only for deaf people but for non-impaired ones in noisy and/or crowded places. However, live subtitling is a high cost operation in terms of high qualification human resources and thus, money if high precision is desired. Even assisted systems (that are partially automatic needing human assistance) could result too expensive compared with a fully automatic system. Automatic Speech Recognition researchers can help to perform this task saving both time and money developing systems that delivers subtitles fully synchronized with the announcer speech without human assistance. In this paper we present a real-time system for automatic subtitling of broadcast news programs in Spanish based on the news redaction computer systems (NRCS) texts and an automatic speech recognition engine to allow proper temporal alignment.

Audio and text synchronization for TV news subtitling based on Automatic Speech Recognition

Currently, most Speaker Verification (SV) systems based on neural networks use Cross-Entropy and/or Triplet loss functions. Despite these functions provide competitive results, they might not fully exploit the system performance, because they are not designed to optimize the verification task considering the performance measures, e.g. the Detection Cost Function (DCF) or the Equal Error Rate (EER). This paper proposes a first approach to this issue through the optimization of a loss function based on the DCF. This mechanism allows the end-toend system to directly manage the threshold used to compute the ratio between the False Rejection Rate (FRR) and the False Acceptance Rate (FAR). This way connecting the system training directly to the operating point. Results in a text-dependent speaker verification framework, based on neural network supervectors over the RSR2015 dataset, outperform reference systems using Cross-Entropy and Triplet loss, as well as our previously proposal based on an approximation of the Area Under the Curve (aAUC).

Optimization of False Acceptance/Rejection Rates and Decision Threshold for End-to-End Text-Dependent Speaker Verification Systems.

The IberSpeech-RTVE Challenge presented at IberSpeech 2018 is a new Albayzin evaluation series supported by the Spanish Thematic Network on Speech Technologies (Red Tematica en Tecnologias del Habla (RTTH)). That series was focused on speech-to-text transcription, speaker diarization, and multimodal diarization of television programs. For this purpose, the Corporacion Radio Television Espanola (RTVE), the main public service broadcaster in Spain, and the RTVE Chair at the University of Zaragoza made more than 500 h of broadcast content and subtitles available for scientists. The dataset included about 20 programs of different kinds and topics produced and broadcast by RTVE between 2015 and 2018. The programs presented different challenges from the point of view of speech technologies such as: the diversity of Spanish accents, overlapping speech, spontaneous speech, acoustic variability, background noise, or specific vocabulary. This paper describes the database and the evaluation process and summarizes the results obtained.

Albayzin 2018 Evaluation: The IberSpeech-RTVE Challenge on Speech Technologies for Spanish Broadcast Media

A multi-environment adaptation technique, based on minimum mean squared error estimation, is proposed. MEMLIN (multi-environment models based linear normalization) consists of a feature adaptation using stereo data and several basic defined environments. The target of this algorithm is to learn the difference between clean and noisy feature vectors associated to a pair of Gaussians (one for a clean model, and the other for a noisy model), for each basic environment. This knowledge, the associated Gaussians, the conditional probability between clean and noisy Gaussians, and the environment are the data used to compensate the mismatch between clean and noisy vectors. This algorithm obtains important improvements regarding other techniques that look for similar targets. The experimental results with the SpeechDat Car database shows an average improvement of more than 68%, concerning the baseline, over 7 different defined environments.

Multi-environment models based linear normalization for speech recognition in car conditions

This paper presents a cabin car communication system (CCCS) to improve the communication among passengers inside a car. Noise, distance between speakers and many other factors make difficult to maintain a conversation inside a car. The CCCS picks up the speech of each passenger, amplifies it, and uses the car loudspeaker system to return it into the cabin. Two problems arise when designing a CCCS; the electro-acoustic coupling between loudspeakers and microphones, and the amplification of the inside car noise. As a result of the first problem, the system may become unstable. To maintain the stability of the system, the CCCS makes use of a robust acoustic echo cancellation scheme based on system identification and a Wiener echo suppressor. Using a noise reduction system based on Wiener filtering reduces second problem, noise amplification. Experimental results showing the performance of the system in terms of acoustic echo and noise reduction and speech reinforce are presented. A system with 2-input/2-output channels has been built on a DSP board for medium size cars and minivan vehicles.

Eduardo Lleida

Papers

Audio and text synchronization for TV news subtitling based on Automatic Speech Recognition

Optimization of False Acceptance/Rejection Rates and Decision Threshold for End-to-End Text-Dependent Speaker Verification Systems.

Albayzin 2018 Evaluation: The IberSpeech-RTVE Challenge on Speech Technologies for Spanish Broadcast Media

Multi-environment models based linear normalization for speech recognition in car conditions

Cabin car communication system to improve communications inside a car