Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices

Deep learning (DL) is characterised by its dynamic nature, with new deep neural network (DNN) architectures and approaches emerging every few years, driving the field's advancement. At the same time, the ever-increasing use of mobile devices (MDs) has resulted in a surge of DNN-based mobile applications. Although traditional architectures, like CNNs and RNNs, have been successfully integrated into MDs, this is not the case for Transformers, a relatively new model family that has achieved new levels of accuracy across AI tasks, but poses significant computational challenges. In this work, we aim to make steps towards bridging this gap by examining the current state of Transformers' on-device execution. To this end, we construct a benchmark of representative models and thoroughly evaluate their performance across MDs with different computational capabilities. Our experimental results show that Transformers are not accelerator-friendly and indicate the need for software and hardware optimisations to achieve efficient deployment.Comment: Accepted at the 3rd IEEE International Workshop on Distributed Intelligent Systems (DistInSys), 202

Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 million (M) hours spanning over 300 languages, and fine-tuning on a smaller labeled dataset. We use multilingual pre-training with random-projection quantization and speech-text modality matching to achieve state-of-the-art performance on downstream multilingual ASR and speech-to-text translation tasks. We also demonstrate that despite using a labeled training set 1/7-th the size of that used for the Whisper model, our model exhibits comparable or better performance on both in-domain and out-of-domain speech recognition tasks across many languages.Comment: 20 pages, 7 figures, 8 table

Transformer Models for Machine Translation and Streaming Automatic Speech Recognition

[ES] El procesamiento del lenguaje natural (NLP) es un conjunto de problemas computacionales con aplicaciones de máxima relevancia, que junto con otras tecnologías informáticas se ha beneficiado de la revolución que ha significado el aprendizaje profundo. Esta tesis se centra en dos problemas fundamentales para el NLP: la traducción automática (MT) y el reconocimiento automático del habla o transcripción automática (ASR); así como en una arquitectura neuronal profunda, el Transformer, que pondremos en práctica para mejorar las soluciones de MT y ASR en algunas de sus aplicaciones. El ASR y MT pueden servir para obtener textos multilingües de alta calidad a un coste razonable para una diversidad de contenidos audiovisuales. Concre- tamente, esta tesis aborda problemas como el de traducción de noticias o el de subtitulación automática de televisión. El ASR y MT también se pueden com- binar entre sí, generando automáticamente subtítulos traducidos, o con otras soluciones de NLP: resumen de textos para producir resúmenes de discursos, o síntesis del habla para crear doblajes automáticos. Estas aplicaciones quedan fuera del alcance de esta tesis pero pueden aprovechar las contribuciones que contiene, en la meduda que ayudan a mejorar el rendimiento de los sistemas automáticos de los que dependen. Esta tesis contiene una aplicación de la arquitectura Transformer al MT tal y como fue concebida, mediante la que obtenemos resultados de primer nivel en traducción de lenguas semejantes. En capítulos subsecuentes, esta tesis aborda la adaptación del Transformer como modelo de lenguaje para sistemas híbri- dos de ASR en vivo. Posteriormente, describe la aplicación de este tipus de sistemas al caso de uso de subtitulación de televisión, participando en una com- petición pública de RTVE donde obtenemos la primera posición con un marge importante. También demostramos que la mejora se debe principalmenta a la tecnología desarrollada y no tanto a la parte de los datos.[CA] El processament del llenguage natural (NLP) és un conjunt de problemes com- putacionals amb aplicacions de màxima rellevància, que juntament amb al- tres tecnologies informàtiques s'ha beneficiat de la revolució que ha significat l'impacte de l'aprenentatge profund. Aquesta tesi se centra en dos problemes fonamentals per al NLP: la traducció automàtica (MT) i el reconeixement automàtic de la parla o transcripció automàtica (ASR); així com en una ar- quitectura neuronal profunda, el Transformer, que posarem en pràctica per a millorar les solucions de MT i ASR en algunes de les seues aplicacions. l'ASR i MT poden servir per obtindre textos multilingües d'alta qualitat a un cost raonable per a un gran ventall de continguts audiovisuals. Concretament, aquesta tesi aborda problemes com el de traducció de notícies o el de subtitu- lació automàtica de televisió. l'ASR i MT també es poden combinar entre ells, generant automàticament subtítols traduïts, o amb altres solucions de NLP: amb resum de textos per produir resums de discursos, o amb síntesi de la parla per crear doblatges automàtics. Aquestes altres aplicacions es troben fora de l'abast d'aquesta tesi però poden aprofitar les contribucions que conté, en la mesura que ajuden a millorar els resultats dels sistemes automàtics dels quals depenen. Aquesta tesi conté una aplicació de l'arquitectura Transformer al MT tal com va ser concebuda, mitjançant la qual obtenim resultats de primer nivell en traducció de llengües semblants. En capítols subseqüents, aquesta tesi aborda l'adaptació del Transformer com a model de llenguatge per a sistemes híbrids d'ASR en viu. Posteriorment, descriu l'aplicació d'aquest tipus de sistemes al cas d'ús de subtitulació de continguts televisius, participant en una competició pública de RTVE on obtenim la primera posició amb un marge significant. També demostrem que la millora es deu principalment a la tecnologia desen- volupada i no tant a la part de les dades[EN] Natural language processing (NLP) is a set of fundamental computing prob- lems with immense applicability, as language is the natural communication vehicle for people. NLP, along with many other computer technologies, has been revolutionized in recent years by the impact of deep learning. This thesis is centered around two keystone problems for NLP: machine translation (MT) and automatic speech recognition (ASR); and a common deep neural architec- ture, the Transformer, that is leveraged to improve the technical solutions for some MT and ASR applications. ASR and MT can be utilized to produce cost-effective, high-quality multilin- gual texts for a wide array of media. Particular applications pursued in this thesis are that of news translation or that of automatic live captioning of tele- vision broadcasts. ASR and MT can also be combined with each other, for instance generating automatic translated subtitles from audio, or augmented with other NLP solutions: text summarization to produce a summary of a speech, or speech synthesis to create an automatic translated dubbing, for in- stance. These other applications fall out of the scope of this thesis, but can profit from the contributions that it contains, as they help to improve the performance of the automatic systems on which they depend. This thesis contains an application of the Transformer architecture to MT as it was originally conceived, achieving state-of-the-art results in similar language translation. In successive chapters, this thesis covers the adaptation of the Transformer as a language model for streaming hybrid ASR systems. After- wards, it describes how we applied the developed technology for a specific use case in television captioning by participating in a competitive challenge and achieving the first position by a large margin. We also show that the gains came mostly from the improvement in technology capabilities over two years including that of the Transformer language model adapted for streaming, and the data component was minor.Baquero Arnal, P. (2023). Transformer Models for Machine Translation and Streaming Automatic Speech Recognition [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19368

Identification of Factors Contributing to Traffic Crashes by Analysis of Text Narratives

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature of text data within narratives has challenged exploration of crash narratives. In response, this dissertation aims to develop an analysis framework and methods to enable the extraction of insights from crash narratives and thus improve our level of understanding of traffic crashes to a new level. The methodological development of this dissertation is split into three objectives. The first objective is to devise an approach for extraction of severity contributing insights from crash narratives by investigating interpretable machine learning and text mining techniques. The second objective is to enable an enhanced identification of crash severity contributors in the form of meaningful phrases by integrating recent advancements in Natural Language Processing (NLP). The third objective is to develop an approach for semantic search of information of interest in crash narratives. The obtained results indicate that the developed approaches enable the extraction of valuable insights from crash narratives to 1) uncover factors that quantitative may not reveal, 2) confirm results from classic statistical analysis on crash data, and 3) fix inconsistencies in quantitative data. The outcomes of this dissertation add substantial value to traffic safety, as the developed approaches allow analysts to exploit the rich information in crash narratives for a more comprehensive and accurate diagnosis of traffic crashes

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis