Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model

Multilingual models for Automatic Speech Recognition (ASR) are attractive as they have been shown to benefit from more training data, and better lend themselves to adaptation to under-resourced languages. However, initialisation from monolingual context-dependent models leads to an explosion of context-dependent states. Connectionist Temporal Classification (CTC) is a potential solution to this as it performs well with monophone labels. We investigate multilingual CTC in the context of adaptation and regularisation techniques that have been shown to be beneficial in more conventional contexts. The multilingual model is trained to model a universal International Phonetic Alphabet (IPA)-based phone set using the CTC loss function. Learning Hidden Unit Contribution (LHUC) is investigated to perform language adaptive training. In addition, dropout during cross-lingual adaptation is also studied and tested in order to mitigate the overfitting problem. Experiments show that the performance of the universal phoneme-based CTC system can be improved by applying LHUC and it is extensible to new phonemes during cross-lingual adaptation. Updating all the parameters shows consistent improvement on limited data. Applying dropout during adaptation can further improve the system and achieve competitive performance with Deep Neural Network / Hidden Markov Model (DNN/HMM) systems on limited data

Fathers, sons and schools: historical dilemmas and future possibilities

Fathers, sons and schools: historical dilemmas and future possibilities is an Invited Paper to the Second International Scientific Conference on 'Parenthood in the face of difficult situations. Disability – Neglect – Disintegration’. It provides an overview of recent research whilst proposing a number of areas (including masculinity, nurturing activity, SEN & disability and 'young dads') around which broader issues of fatherhood are explored

Including children with special educational needs: how can we better prepare teachers to respond to the challenges and opportunities?

This presentation is an Invited Keynote Presentation at the Special Education International Congress on Educational Leadership & Management in Special Education (ELMIS) Aksehir, Turkey, 19-21 June, 2013. It explores the personal, professional and interactional dimensions that generic inform or inhibit the development of inclusive classrooms; these are discussed in the context of educational leadership

Reflect, shift, transform

This Invited Keynote presentation, to the First Asia Pacific Conference on Creating Inclusive Schools (hosted by the Australian Council of Educational Leaders and the Australian Special Education Principals Association), seeks to highlight the key inhibitors and catalysts of change in schools. The intention is to question established practices, challenge current orthodoxies and highlight some ways in which schools and education systems should be working to make provision more inclusive

School Leadership and professional development: the challenge of challenging behaviour

This paper reports on a recently funded study which sought to identify the key themes which support school leaders in promoting positive student behaviour. Widespread concern is being expressed across Europe regarding a perceived increase in cases of unacceptable or anti-social behaviour in schools and its impact on levels of academic achievement. The issue has mainly been interrogated via research and development which has sought to enhance provision at initial teacher level. Yet international studies on ‘school discipline’ indicate that it is the school leader who is the key actor in promoting positive attitudes to learning and behaviour. This paper explores the views of a small group of school leaders, in order to identify specific professional and personal characteristics which appear to be required to promote such conditions. The implications and potential use of these findings for the professional development of school leaders is then considered, using examples drawn from the project

The trouble with behaviour: lessons from the past and pointers to the future

This invited keynote presentation examines the myths, tensions and challenges which underpin provision for students who present behaviour challenges in schools. It interrogates the nature of long-standing professional beliefs & truisms and looks at recent policy trajectories as well as pointing to possible points of progression

Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees

This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing entries based on a novel Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and projection onto the Euclidean distance space. This approach confines the recovered matrix to the EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of the calibration performance are obtained considering the random and locally structured missing entries as well as the measurement noise on the known distances. This study elucidates the links between the calibration error and the number of microphones along with the noise level and the ratio of missing distances. Thorough experiments on real data recordings and simulated setups are conducted to demonstrate these theoretical insights. A significant improvement is achieved by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art techniques for ad hoc microphone array calibration.Comment: In Press, available online, August 1, 2014. http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal Processing, 201

Surrogate Gradient Spiking Neural Networks as Encoders for Large Vocabulary Continuous Speech Recognition

Compared to conventional artificial neurons that produce dense and real-valued responses, biologically-inspired spiking neurons transmit sparse and binary information, which can also lead to energy-efficient implementations. Recent research has shown that spiking neural networks can be trained like standard recurrent neural networks using the surrogate gradient method. They have shown promising results on speech command recognition tasks. Using the same technique, we show that they are scalable to large vocabulary continuous speech recognition, where they are capable of replacing LSTMs in the encoder with only minor loss of performance. This suggests that they may be applicable to more involved sequence-to-sequence tasks. Moreover, in contrast to their recurrent non-spiking counterparts, they show robustness to exploding gradient problems without the need to use gates