Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu

Automatic speech recognition (ASR) is potentially helpful for children who suffer from dyslexia. Highly phonetically similar errors of dyslexic children‟s reading affect the accuracy of ASR. Thus, this study aims to evaluate acceptable accuracy of ASR using automatic transcription and phonetic labelling of dyslexic children‟s reading in BM. For that, three objectives have been set: first to produce manual transcription and phonetic labelling; second to construct automatic transcription and phonetic labelling using forced alignment; and third to compare between accuracy using automatic transcription and phonetic labelling and manual transcription and phonetic labelling. Therefore, to accomplish these goals methods have been used including manual speech labelling and segmentation, forced alignment, Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for training, and for measure accuracy of ASR, Word Error Rate (WER) and False Alarm Rate (FAR) were used. A number of 585 speech files are used for manual transcription, forced alignment and training experiment. The recognition ASR engine using automatic transcription and phonetic labelling obtained optimum results is 76.04% with WER as low as 23.96% and FAR is 17.9%. These results are almost similar with ASR engine using manual transcription namely 76.26%, WER as low as 23.97% and FAR a 17.9%. As conclusion, the accuracy of automatic transcription and phonetic labelling is acceptable to use it for help dyslexic children learning using ASR in Bahasa Melayu (BM

Deep neural network acoustic models for multi-dialect Arabic speech recognition

Speech is a desirable communication method between humans and computers. The major concerns of the automatic speech recognition (ASR) are determining a set of classification features and finding a suitable recognition model for these features. Hidden Markov Models (HMMs) have been demonstrated to be powerful models for representing time varying signals. Artificial Neural Networks (ANNs) have also been widely used for representing time varying quasi-stationary signals. Arabic is one of the oldest living languages and one of the oldest Semitic languages in the world, it is also the fifth most generally used language and is the mother tongue for roughly 200 million people. Arabic speech recognition has been a fertile area of reasearch over the previous two decades, as attested by the various papers that have been published on this subject. This thesis investigates phoneme and acoustic models based on Deep Neural Networks (DNN) and Deep Echo State Networks for multi-dialect Arabic Speech Recognition. Moreover, the TIMIT corpus with a wide variety of American dialects is also aimed to evaluate the proposed models. The availability of speech data that is time-aligned and labelled at phonemic level is a fundamental requirement for building speech recognition systems. A developed Arabic phoneme database (APD) was manually timed and phonetically labelled. This dataset was constructed from the King Abdul-Aziz Arabic Phonetics Database (KAPD) database for Saudi Arabia dialect and the Centre for Spoken Language Understanding (CSLU2002) database for different Arabic dialects. This dataset covers 8148 Arabic phonemes. In addition, a corpus of 120 speakers (13 hours of Arabic speech) randomly selected from the Levantine Arabic dialect database that is used for training and 24 speakers (2.4 hours) for testing are revised and transcription errors were manually corrected. The selected dataset is labelled automatically using the HTK Hidden Markov Model toolkit. TIMIT corpus is also used for phone recognition and acoustic modelling task. We used 462 speakers (3.14 hours) for training and 24 speakers (0.81 hours) for testing. For Automatic Speech Recognition (ASR), a Deep Neural Network (DNN) is used to evaluate its adoption in developing a framewise phoneme recognition and an acoustic modelling system for Arabic speech recognition. Restricted Boltzmann Machines (RBMs) DNN models have not been explored for any Arabic corpora previously. This allows us to claim priority for adopting this RBM DNN model for the Levantine Arabic acoustic models. A post-processing enhancement was also applied to the DNN acoustic model outputs in order to improve the recognition accuracy and to obtain the accuracy at a phoneme level instead of the frame level. This post process has significantly improved the recognition performance. An Echo State Network (ESN) is developed and evaluated for Arabic phoneme recognition with different learning algorithms. This investigated the use of the conventional ESN trained with supervised and forced learning algorithms. A novel combined supervised/forced supervised learning algorithm (unsupervised adaptation) was developed and tested on the proposed optimised Arabic phoneme recognition datasets. This new model is evaluated on the Levantine dataset and empirically compared with the results obtained from the baseline Deep Neural Networks (DNNs). A significant improvement on the recognition performance was achieved when the ESN model was implemented compared to the baseline RBM DNN model’s result. The results show that the ESN model has a better ability for recognizing phonemes sequences than the DNN model for a small vocabulary size dataset. The adoption of the ESNs model for acoustic modeling is seen to be more valid than the adoption of the DNNs model for acoustic modeling speech recognition, as ESNs are recurrent models and expected to support sequence models better than the RBM DNN models even with the contextual input window. The TIMIT corpus is also used to investigate deep learning for framewise phoneme classification and acoustic modelling using Deep Neural Networks (DNNs) and Echo State Networks (ESNs) to allow us to make a direct and valid comparison between the proposed systems investigated in this thesis and the published works in equivalent projects based on framewise phoneme recognition used the TIMIT corpus. Our main finding on this corpus is that ESN network outperform time-windowed RBM DNN ones. However, our developed system ESN-based shows 10% lower performance when it was compared to the other systems recently reported in the literature that used the same corpus. This due to the hardware availability and not applying speaker and noise adaption that can improve the results in this thesis as our aim is to investigate the proposed models for speech recognition and to make a direct comparison between these models

Modelo acústico de língua inglesa falada por portugueses

Trabalho de projecto de mestrado em Engenharia Informática, apresentado à Universidade de Lisboa, através da Faculdade de Ciências, 2007No contexto do reconhecimento robusto de fala baseado em modelos de Markov não observáveis (do inglês Hidden Markov Models - HMMs) este trabalho descreve algumas metodologias e experiências tendo em vista o reconhecimento de oradores estrangeiros. Quando falamos em Reconhecimento de Fala falamos obrigatoriamente em Modelos Acústicos também. Os modelos acústicos reflectem a maneira como pronunciamos/articulamos uma língua, modelando a sequência de sons emitidos aquando da fala. Essa modelação assenta em segmentos de fala mínimos, os fones, para os quais existe um conjunto de símbolos/alfabetos que representam a sua pronunciação. É no campo da fonética articulatória e acústica que se estuda a representação desses símbolos, 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 sequência de símbolos codificados. Para isso, o sinal é fragmentado em observações de sensivelmente 10 milissegundos cada, reduzindo assim o factor de análise ao intervalo de tempo onde as características de um segmento de som não variam. Os modelos acústicos dão-nos uma noção sobre a probabilidade de uma determinada observação corresponder a uma determinada entidade. É, portanto, através de modelos sobre as entidades do vocabulário a reconhecer que é possível voltar a juntar esses fragmentos de som. Os modelos desenvolvidos neste trabalho são baseados em HMMs. Chamam-se assim por se fundamentarem nas cadeias de Markov (1856 - 1922): sequências de estados onde cada estado é condicionado pelo seu anterior. Localizando esta abordagem no nosso domínio, há que construir um conjunto de modelos - um para cada classe de sons a reconhecer - que serão treinados por dados de treino. Os dados são ficheiros áudio e respectivas transcrições (ao nível da palavra) de modo a que seja possível decompor essa transcrição em fones e alinhá-la a cada som do ficheiro áudio correspondente. Usando um modelo de estados, onde cada estado representa uma observação ou segmento de fala descrita, os dados vão-se reagrupando de maneira a criar modelos estatísticos, cada vez mais fidedignos, que consistam em representações das entidades da fala de uma determinada língua. O reconhecimento por parte de oradores estrangeiros com pronuncias diferentes da língua para qual o reconhecedor foi concebido, pode ser um grande problema para precisão de um reconhecedor. Esta variação pode ser ainda mais problemática que a variação dialectal de uma determinada língua, isto porque depende do conhecimento que cada orador têm relativamente à língua estrangeira. Usando para uma pequena quantidade áudio de oradores estrangeiros para o treino de novos modelos acústicos, foram efectuadas diversas experiências usando corpora de Portugueses a falar Inglês, de Português Europeu e de Inglês. 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 não nativos). De seguida foi treinado um outro modelo usando em simultâneo como corpus de treino, o áudio de Portugueses a falar Inglês e o de Ingleses nativos. Uma outra experiência levada a cabo teve em conta o uso de técnicas de adaptação, tal como a técnica MLLR, do inglês Maximum Likelihood Linear Regression. Esta última permite a adaptação de uma determinada característica do orador, neste caso o sotaque estrangeiro, a um determinado modelo inicial. Com uma pequena quantidade de dados representando a característica que se quer modelar, esta técnica calcula um conjunto de transformações que serão aplicadas ao modelo que se quer adaptar. Foi também explorado o campo da modelação fonética onde estudou-se como é que o orador estrangeiro pronuncia a língua estrangeira, neste caso um Português a falar Inglês. Este estudo foi feito com a ajuda de um linguista, o qual definiu um conjunto de fones, resultado do mapeamento do inventário de fones do Inglês para o Português, que representam o Inglês falado por Portugueses de um determinado grupo de prestígio. Dada a grande variabilidade de pronúncias teve de se definir este grupo tendo em conta o nível de literacia dos oradores. Este estudo foi posteriormente usado na criação de um novo modelo treinado com os corpora de Portugueses a falar Inglês e de Portugueses nativos. Desta forma representamos um reconhecedor de Português nativo onde o reconhecimento de termos ingleses é possível. Tendo em conta a temática do reconhecimento de fala este projecto focou também a recolha de corpora para português europeu e a compilação de um léxico de Português europeu. Na área de aquisição de corpora o autor esteve envolvido na extracção e preparação dos dados de fala telefónica, para posterior treino de novos modelos acústicos de português europeu. Para compilação do léxico de português europeu usou-se um método incremental semi-automático. Este método 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 automática de pronunciações.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 user’s interfaces. Besides voice enabled Internet portals or tourist information systems, automatic speech recognition systems can be used in home user’s 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 speaker’s mother tongue. Consequently, when the speaker’s 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 speakers’ 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 speakers’ 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

On the use of high-level information in speaker and language recognition

Actas de las IV Jornadas de Tecnología del Habla (JTH 2006)Automatic Speaker Recognition systems have been largely dominated by acoustic-spectral based systems, relying in proper modelling of the short-term vocal tract of speakers. However, there is scientific and intuitive evidence that speaker specific information is embedded in the speech signal in multiple short- and long-term characteristics. In this work, a multilevel speaker recognition system combining acoustic, phonotactic and prosodic subsystems is presented and assessed using NIST 2005 Speaker Recognition Evaluation data. For language recognition systems, the NIST 2005 Language Recognition Evaluation was selected to measure performance of a high-level language recognition systems

Incremental Disfluency Detection for Spoken Learner English

Dialogue-based computer-assisted language learning (CALL) concerns the application and analysis of automated systems that engage with a language learner through dialogue. Routed in an interactionist perspective of second language acquisition, dialogue-based CALL systems assume the role of a speaking partner, providing learners the opportunity for spontaneous production of their second language. One area of interest for such systems is the implementation of corrective feedback. However, the feedback strategies employed by such systems remain fairly limited. In particular, there are currently no provisions for learners to initiate the correction of their own errors, despite this being the most frequently occurring and most preferred type of error correction in learner speech. To address this gap, this thesis proposes a framework for implementing such functionality, identifying incremental self-initiated self-repair (i.e. disfluency) detection as a key area for research. Taking an interdisciplinary approach to the exploration of this topic, this thesis outlines the steps taken to optimise an incremental disfluency detection model for use with spoken learner English. To begin, a linguistic comparative analysis of native and learner disfluency corpora explored the differences between the disfluency behaviour of native and learner speech, highlighting key features of learner speech not previously explored in disfluency detection model analysis. Following this, in order to identify a suitable baseline model for further experimentation, two state-of-the-art incremental self-repair detection models were trained and tested with a learner speech corpus. An error analysis of the models' outputs found an LSTM model using word embeddings and part-of-speech tags to be the most suitable for learner speech, thanks to its lower number of false positives triggered by learner errors in the corpus. Following this, several adaptations to the model were tested to improve performance. Namely, the inclusion of character embeddings, silence and laughter features, separating edit term detection from disfluency detection, lemmatization and the inclusion of learners' prior proficiency scores led to over an eight percent model improvement over the baseline. Findings from this thesis illustrate how the analysis of language characteristics specific to learner speech can positively inform model adaptation and provide a starting point for further investigation into the implementation of effective corrective feedback strategies in dialogue-based CALL systems