430 research outputs found
From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy
This chapter addresses the following question: What are the advantages of extending a fuzzy expert system (FES) to an artificial neural network (ANN), within a computerâbased speech therapy system (CBST)? We briefly describe the key concepts underlying the principles behind the FES and ANN and their applications in assisted speech therapy. We explain the importance of an intelligent system in order to design an appropriate model for realâlife situations. We present data from 1âyear application of these concepts in the field of assisted speech therapy. Using an artificial intelligent system for improving speech would allow designing a training program for pronunciation, which can be individualized based on specialty needs, previous experiences, and the child\u27s prior therapeutical progress. Neural networks add a great plus value when dealing with data that do not normally match our previous designed pattern. Using an integrated approach that combines FES and ANN allows our system to accomplish three main objectives: (1) develop a personalized therapy program; (2) gradually replace some human expert duties; (3) use âselfâlearningâ capabilities, a component traditionally reserved for humans. The results demonstrate the viability of the hybrid approach in the context of speech therapy that can be extended when designing similar applications
Computational and Numerical Simulations
Computational and Numerical Simulations is an edited book including 20 chapters. Book handles the recent research devoted to numerical simulations of physical and engineering systems. It presents both new theories and their applications, showing bridge between theoretical investigations and possibility to apply them by engineers of different branches of science. Numerical simulations play a key role in both theoretical and application oriented research
Rhythmic unit extraction and modelling for automatic language identification
International audienceThis paper deals with an approach to Automatic Language Identification based on rhythmic modelling. Beside phonetics and phonotactics, rhythm is actually one of the most promising features to be considered for language identification, even if its extraction and modelling are not a straightforward issue. Actually, one of the main problems to address is what to model. In this paper, an algorithm of rhythm extraction is described: using a vowel detection algorithm, rhythmic units related to syllables are segmented. Several parameters are extracted (consonantal and vowel duration, cluster complexity) and modelled with a Gaussian Mixture. Experiments are performed on read speech for 7 languages (English, French, German, Italian, Japanese, Mandarin and Spanish) and results reach up to 86 ± 6% of correct discrimination between stress-timed mora-timed and syllable-timed classes of languages, and to 67 ± 8% percent of correct language identification on average for the 7 languages with utterances of 21 seconds. These results are commented and compared with those obtained with a standard acoustic Gaussian mixture modelling approach (88 ± 5% of correct identification for the 7-languages identification task)
Modelo acĂșstico de lĂngua inglesa falada por portugueses
Trabalho de projecto de mestrado em Engenharia InformĂĄtica, apresentado Ă Universidade de Lisboa, atravĂ©s da Faculdade de CiĂȘncias, 2007No contexto do reconhecimento robusto de fala baseado em modelos de Markov nĂŁo observĂĄveis (do inglĂȘs Hidden Markov Models - HMMs) este trabalho descreve algumas metodologias e experiĂȘncias tendo em vista o reconhecimento de oradores estrangeiros. Quando falamos em Reconhecimento de Fala falamos obrigatoriamente em Modelos AcĂșsticos tambĂ©m. Os modelos acĂșsticos reflectem a maneira como pronunciamos/articulamos uma lĂngua, modelando a sequĂȘncia de sons emitidos aquando da fala. Essa modelação assenta em segmentos de fala mĂnimos, os fones, para os quais existe um conjunto de sĂmbolos/alfabetos que representam a sua pronunciação. Ă no campo da fonĂ©tica articulatĂłria e acĂșstica que se estuda a representação desses sĂmbolos, sua articulação e pronunciação. Conseguimos descrever palavras analisando as unidades que as constituem, os fones. Um reconhecedor de fala interpreta o sinal de entrada, a fala, como uma sequĂȘncia de sĂmbolos codificados. Para isso, o sinal Ă© fragmentado em observaçÔes de sensivelmente 10 milissegundos cada, reduzindo assim o factor de anĂĄlise ao intervalo de tempo onde as caracterĂsticas de um segmento de som nĂŁo variam. Os modelos acĂșsticos dĂŁo-nos uma noção sobre a probabilidade de uma determinada observação corresponder a uma determinada entidade. Ă, portanto, atravĂ©s de modelos sobre as entidades do vocabulĂĄrio a reconhecer que Ă© possĂvel voltar a juntar esses fragmentos de som. Os modelos desenvolvidos neste trabalho sĂŁo baseados em HMMs. Chamam-se assim por se fundamentarem nas cadeias de Markov (1856 - 1922): sequĂȘncias de estados onde cada estado Ă© condicionado pelo seu anterior. Localizando esta abordagem no nosso domĂnio, hĂĄ que construir um conjunto de modelos - um para cada classe de sons a reconhecer - que serĂŁo treinados por dados de treino. Os dados sĂŁo ficheiros ĂĄudio e respectivas transcriçÔes (ao nĂvel da palavra) de modo a que seja possĂvel decompor essa transcrição em fones e alinhĂĄ-la a cada som do ficheiro ĂĄudio correspondente. Usando um modelo de estados, onde cada estado representa uma observação ou segmento de fala descrita, os dados vĂŁo-se reagrupando de maneira a criar modelos estatĂsticos, cada vez mais fidedignos, que consistam em representaçÔes das entidades da fala de uma determinada lĂngua. O reconhecimento por parte de oradores estrangeiros com pronuncias diferentes da lĂngua para qual o reconhecedor foi concebido, pode ser um grande problema para precisĂŁo de um reconhecedor. Esta variação pode ser ainda mais problemĂĄtica que a variação dialectal de uma determinada lĂngua, isto porque depende do conhecimento que cada orador tĂȘm relativamente Ă lĂngua estrangeira. Usando para uma pequena quantidade ĂĄudio de oradores estrangeiros para o treino de novos modelos acĂșsticos, foram efectuadas diversas experiĂȘncias usando corpora de Portugueses a falar InglĂȘs, de PortuguĂȘs Europeu e de InglĂȘs. Inicialmente foi explorado o comportamento, separadamente, dos modelos de Ingleses nativos e Portugueses nativos, quando testados com os corpora de teste (teste com nativos e teste com nĂŁo nativos). De seguida foi treinado um outro modelo usando em simultĂąneo como corpus de treino, o ĂĄudio de Portugueses a falar InglĂȘs e o de Ingleses nativos. Uma outra experiĂȘncia levada a cabo teve em conta o uso de tĂ©cnicas de adaptação, tal como a tĂ©cnica MLLR, do inglĂȘs Maximum Likelihood Linear Regression. Esta Ășltima permite a adaptação de uma determinada caracterĂstica do orador, neste caso o sotaque estrangeiro, a um determinado modelo inicial. Com uma pequena quantidade de dados representando a caracterĂstica que se quer modelar, esta tĂ©cnica calcula um conjunto de transformaçÔes que serĂŁo aplicadas ao modelo que se quer adaptar. Foi tambĂ©m explorado o campo da modelação fonĂ©tica onde estudou-se como Ă© que o orador estrangeiro pronuncia a lĂngua estrangeira, neste caso um PortuguĂȘs a falar InglĂȘs. Este estudo foi feito com a ajuda de um linguista, o qual definiu um conjunto de fones, resultado do mapeamento do inventĂĄrio de fones do InglĂȘs para o PortuguĂȘs, que representam o InglĂȘs falado por Portugueses de um determinado grupo de prestĂgio. Dada a grande variabilidade de pronĂșncias teve de se definir este grupo tendo em conta o nĂvel de literacia dos oradores. Este estudo foi posteriormente usado na criação de um novo modelo treinado com os corpora de Portugueses a falar InglĂȘs e de Portugueses nativos. Desta forma representamos um reconhecedor de PortuguĂȘs nativo onde o reconhecimento de termos ingleses Ă© possĂvel. Tendo em conta a temĂĄtica do reconhecimento de fala este projecto focou tambĂ©m a recolha de corpora para portuguĂȘs europeu e a compilação de um lĂ©xico de PortuguĂȘs europeu. Na ĂĄrea de aquisição de corpora o autor esteve envolvido na extracção e preparação dos dados de fala telefĂłnica, para posterior treino de novos modelos acĂșsticos de portuguĂȘs europeu. Para compilação do lĂ©xico de portuguĂȘs europeu usou-se um mĂ©todo incremental semi-automĂĄtico. Este mĂ©todo consistiu em gerar automaticamente a pronunciação de grupos de 10 mil palavras, sendo cada grupo revisto e corrigido por um linguista. Cada grupo de palavras revistas era posteriormente usado para melhorar as regras de geração automĂĄtica de pronunciaçÔes.The tremendous growth of technology has increased the need of integration of spoken language technologies into our daily applications, providing an easy and natural access to information. These applications are of different nature with different userâs interfaces. Besides voice enabled Internet portals or tourist information systems, automatic speech recognition systems can be used in home userâs experiences where TV and other appliances could be voice controlled, discarding keyboards or mouse interfaces, or in mobile phones and palm-sized computers for a hands-free and eyes-free manipulation. The development of these systems causes several known difficulties. One of them concerns the recognizer accuracy on dealing with non-native speakers with different phonetic pronunciations of a given language. The non-native accent can be more problematic than a dialect variation on the language. This mismatch depends on the individual speaking proficiency and speakerâs mother tongue. Consequently, when the speakerâs native language is not the same as the one that was used to train the recognizer, there is a considerable loss in recognition performance. In this thesis, we examine the problem of non-native speech in a speaker-independent and large-vocabulary recognizer in which a small amount of non-native data was used for training. Several experiments were performed using Hidden Markov models, trained with speech corpora containing European Portuguese native speakers, English native speakers and English spoken by European Portuguese native speakers. Initially it was explored the behaviour of an English native model and non-native English speakersâ model. Then using different corpus weights for the English native speakers and English spoken by Portuguese speakers it was trained a model as a pool of accents. Through adaptation techniques it was used the Maximum Likelihood Linear Regression method. It was also explored how European Portuguese speakers pronounce English language studying the correspondences between the phone sets of the foreign and target languages. The result was a new phone set, consequence of the mapping between the English and the Portuguese phone sets. Then a new model was trained with English Spoken by Portuguese speakersâ data and Portuguese native data. Concerning the speech recognition subject this work has other two purposes: collecting Portuguese corpora and supporting the compilation of a Portuguese lexicon, adopting some methods and algorithms to generate automatic phonetic pronunciations. The collected corpora was processed in order to train acoustic models to be used in the Exchange 2007 domain, namely in Outlook Voice Access
Impact of dialect use on a basic component of learning to read
Can some black-white differences in reading achievement be traced to differences in language background? Many African American children speak a dialect that differs from the mainstream dialect emphasized in school. We examined how use of alternative dialects affects decoding, an important component of early reading and marker of reading development. Behavioral data show that use of the alternative pronunciations of words in different dialects affects reading aloud in developing readers, with larger effects for children who use more African American English. Mechanisms underlying this effect were explored with a computational model, investigating factors affecting reading acquisition. The results indicate that the achievement gap may be due in part to differences in task complexity: children whose home and school dialects differ are at greater risk for reading difficulties because tasks such as learning to decode are more complex for them
DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation
In recent years, audio-driven 3D facial animation has gained significant
attention, particularly in applications such as virtual reality, gaming, and
video conferencing. However, accurately modeling the intricate and subtle
dynamics of facial expressions remains a challenge. Most existing studies
approach the facial animation task as a single regression problem, which often
fail to capture the intrinsic inter-modal relationship between speech signals
and 3D facial animation and overlook their inherent consistency. Moreover, due
to the limited availability of 3D-audio-visual datasets, approaches learning
with small-size samples have poor generalizability that decreases the
performance. To address these issues, in this study, we propose a cross-modal
dual-learning framework, termed DualTalker, aiming at improving data usage
efficiency as well as relating cross-modal dependencies. The framework is
trained jointly with the primary task (audio-driven facial animation) and its
dual task (lip reading) and shares common audio/motion encoder components. Our
joint training framework facilitates more efficient data usage by leveraging
information from both tasks and explicitly capitalizing on the complementary
relationship between facial motion and audio to improve performance.
Furthermore, we introduce an auxiliary cross-modal consistency loss to mitigate
the potential over-smoothing underlying the cross-modal complementary
representations, enhancing the mapping of subtle facial expression dynamics.
Through extensive experiments and a perceptual user study conducted on the VOCA
and BIWI datasets, we demonstrate that our approach outperforms current
state-of-the-art methods both qualitatively and quantitatively. We have made
our code and video demonstrations available at
https://github.com/sabrina-su/iadf.git
The Impact of AI on Teaching and Learning in Higher Education Technology
Thanks to AI, students may now study whenever and wherever they like. Personalized feedback on assignments, quizzes, and other assessments can be generated using AI algorithms and utilised as a teaching tool to help students succeed. This study examined the impact of artificial intelligence in higher education teaching and learning. This study focuses on the impact of new technologies on student learning and educational institutions. With the rapid adoption of new technologies in higher education, as well as recent technological advancements, it is possible to forecast the future of higher education in a world where artificial intelligence is ubiquitous. Administration, student support, teaching, and learning can all benefit from the use of these technologies; we identify some challenges that higher education institutions and students may face, and we consider potential research directions
Integrating Language Identification to improve Multilingual Speech Recognition
The process of determining the language of a speech utterance is called Language Identification (LID). This task can be very challenging as it has to take into account various language-specific aspects, such as phonetic, phonotactic, vocabulary and grammar-related cues. In multilingual speech recognition we try to find the most likely word sequence that corresponds to an utterance where the language is not known a priori. This is a considerably harder task compared to monolingual speech recognition and it is common to use LID to estimate the current language. In this project we present two general approaches for LID and describe how to integrate them into multilingual speech recognizers. The first approach uses hierarchical multilayer perceptrons to estimate language posterior probabilities given the acoustics in combination with hidden Markov models. The second approach evaluates the output of a multilingual speech recognizer to determine the spoken language. The research is applied to the MediaParl speech corpus that was recorded at the Parliament of the canton of Valais, where people switch from Swiss French to Swiss German or vice versa. Our experiments show that, on that particular data set, LID can be used to significantly improve the performance of multilingual speech recognizers. We will also point out that ASR dependent LID approaches yield the best performance due to higher-level cues and that our systems perform much worse on non-native dat
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