Saudi Accented Arabic Voice Bank

AbstractThe aim of this paper is to present an Arabic speech database that represents Arabic native speakers from all the cities of Saudi Arabia. The database is called the Saudi Accented Arabic Voice Bank (SAAVB). Preparing the prompt sheets, selecting the right speakers and transcribing their speech are some of the challenges that faced the project team. The procedures that meet these challenges are highlighted. SAAVB consists of 1033 speakers speak in Modern Standard Arabic with a Saudi accent. The SAAVB content is analyzed and the results are illustrated. The content was verified internally and externally by IBM Cairo and can be used to train speech engines such as automatic speech recognition and speaker verification systems

PHONOTACTIC AND ACOUSTIC LANGUAGE RECOGNITION

Práce pojednává o fonotaktickém a akustickém přístupu pro automatické rozpoznávání jazyka. První část práce pojednává o fonotaktickém přístupu založeném na výskytu fonémových sekvenci v řeči. Nejdříve je prezentován popis vývoje fonémového rozpoznávače jako techniky pro přepis řeči do sekvence smysluplných symbolů. Hlavní důraz je kladen na dobré natrénování fonémového rozpoznávače a kombinaci výsledků z několika fonémových rozpoznávačů trénovaných na různých jazycích (Paralelní fonémové rozpoznávání následované jazykovými modely (PPRLM)). Práce také pojednává o nové technice anti-modely v PPRLM a studuje použití fonémových grafů místo nejlepšího přepisu. Na závěr práce jsou porovnány dva přístupy modelování výstupu fonémového rozpoznávače -- standardní n-gramové jazykové modely a binární rozhodovací stromy. Hlavní přínos v akustickém přístupu je diskriminativní modelování cílových modelů jazyků a první experimenty s kombinací diskriminativního trénování a na příznacích, kde byl odstraněn vliv kanálu. Práce dále zkoumá různé druhy technik fúzi akustického a fonotaktického přístupu. Všechny experimenty jsou provedeny na standardních datech z NIST evaluaci konané v letech 2003, 2005 a 2007, takže jsou přímo porovnatelné s výsledky ostatních skupin zabývajících se automatickým rozpoznáváním jazyka. S fúzí uvedených technik jsme posunuli state-of-the-art výsledky a dosáhli vynikajících výsledků ve dvou NIST evaluacích.This thesis deals with phonotactic and acoustic techniques for automatic language recognition (LRE). The first part of the thesis deals with the phonotactic language recognition based on co-occurrences of phone sequences in speech. A thorough study of phone recognition as tokenization technique for LRE is done, with focus on the amounts of training data for phone recognizer and on the combination of phone recognizers trained on several language (Parallel Phone Recognition followed by Language Model - PPRLM). The thesis also deals with novel technique of anti-models in PPRLM and investigates into using phone lattices instead of strings. The work on phonotactic approach is concluded by a comparison of classical n-gram modeling techniques and binary decision trees. The acoustic LRE was addressed too, with the main focus on discriminative techniques for training target language acoustic models and on initial (but successful) experiments with removing channel dependencies. We have also investigated into the fusion of phonotactic and acoustic approaches. All experiments were performed on standard data from NIST 2003, 2005 and 2007 evaluations so that the results are directly comparable to other laboratories in the LRE community. With the above mentioned techniques, the fused systems defined the state-of-the-art in the LRE field and reached excellent results in NIST evaluations.

Deep learning for speech to text transcription for the portuguese language

Automatic speech recognition (ASR) is the process of transcribing audio recordings into text, i.e. to transform speech into the respective sequence of words. This process is also commonly known as speechto- text. Machine learning (ML), the ability of machines to learn from examples, is one of the most relevant areas of artificial intelligence in today’s world. Deep learning is a subset of ML which makes use of Deep Neural Networks, a particular type of Artificial Neural Networks (ANNs), which are intended to mimic human neurons, that possess a large number of layers. This dissertation reviews the state-of-the-art on automatic speech recognition throughout time, from early systems which used Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) to the most up-to-date end-to-end (E2E) deep neural models. Considering the context of the present work, some deep learning algorithms used in state-of-the-art approaches are explained in additional detail. The current work aims to develop an ASR system for the European Portuguese language using deep learning. This is achieved by implementing a pipeline composed of stages responsible for data acquisition, data analysis, data pre-processing, model creation and evaluation of results. With the NVIDIA NeMo framework was possible to implement the QuartzNet15x5 architecture based on 1D time-channel separable convolutions. Following a data-centric methodology, the model developed yielded state-of-the-art Word Error Rate (WER) results of WER = 0.0503; Sumário: Aprendizagem profunda para transcrição de fala para texto para a Língua Portuguesa - O reconhecimento automático de fala (ASR) é o processo de transcrever gravações de áudio em texto, i.e., transformar a fala na respectiva sequência de palavras. Esse processo também é comumente conhecido como speech-to-text. A aprendizagem de máquina (ML), a capacidade das máquinas de aprenderem através de exemplos, é um dos campos mais relevantes da inteligência artificial no mundo atual. Deep learning é um subconjunto de ML que faz uso de Redes Neurais Profundas, um tipo particular de Redes Neurais Artificiais (ANNs), que se destinam a imitar neurónios humanos, que possuem um grande número de camadas Esta dissertação faz uma revisão ao estado da arte do reconhecimento automático de fala ao longo do tempo, desde os primeiros sistemas que usavam Hidden Markov Models (HMMs) e Gaussian Mixture Models (GMMs até sistemas end-to-end (E2E) mais recentes que usam modelos neuronais profundos. Considerando o contexto do presente trabalho, alguns algoritmos de aprendizagem profunda usados em abordagens de ponta são explicados mais detalhadamente. O presente trabalho tem como objetivo desenvolver um sistema ASR para a língua portuguesa europeia utilizando deep learning. Isso é conseguido por meio da implementação de um pipeline composto por etapas responsáveis pela aquisição de dados, análise dos dados, pré-processamento dos dados, criação do modelo e avaliação dos resultados. Com o framework NVIDIA NeMo foi possível implementar a arquitetura QuartzNet15x5 baseada em convoluções 1D separáveis por canal de tempo. Seguindo uma metodologia centrada em dados, o modelo desenvolvido produziu resultados de taxa de erro de palavra (WER) semelhantes aos de estado da arte de WER = 0.0503

Robust ASR using Support Vector Machines

The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units. In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841–1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad

Report on first selection of resources

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.Peer ReviewedPreprin

The European Language Resources and Technologies Forum: Shaping the Future of the Multilingual Digital Europe

Proceedings of the 1st FLaReNet Forum on the European Language Resources and Technologies, held in Vienna, at the Austrian Academy of Science, on 12-13 February 2009

Design, development and field evaluation of a Spanish into sign language translation system

This paper describes the design, development and field evaluation of a machine translation system from Spanish to Spanish Sign Language (LSE: Lengua de Signos Española). The developed system focuses on helping Deaf people when they want to renew their Driver’s License. The system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs). For the natural language translator, three technological approaches have been implemented and evaluated: an example-based strategy, a rule-based translation method and a statistical translator. For the final version, the implemented language translator combines all the alternatives into a hierarchical structure. This paper includes a detailed description of the field evaluation. This evaluation was carried out in the Local Traffic Office in Toledo involving real government employees and Deaf people. The evaluation includes objective measurements from the system and subjective information from questionnaires. The paper details the main problems found and a discussion on how to solve them (some of them specific for LSE)