In traditional foreign language learning programmes students are offered a tutor model characterised by slow, deliberate speech. This is insufficient to prepare them to cope with native, colloquial speech. By applying a timescale modification (TSM) algorithm to natural-speed, native speech, students have access to a more desirable, natural speech corpus which permits them to practise essential ...

The present study investigated the effect of using PRAAT as a free computer software package for the scientific analysis of speech in phonetics on pre-intermediate Iranian English as foreign language (EFL) learners’ supra segmental features (i.e., intonation and stress). The design of the study was a Quasi-experimental research design with a pre and post-test. In doing so...

The use of present-day speech recognition techniques in many practical applications has demonstrated the need for improved algorithm formulation under varying acoustical environments. This paper describes a low-vocabulary speech recognition algorithm that provides robust performance in noisy environments with particular emphasis on characteristics due to the Lombard effect. A neutral aod stress...

The classification of active speech vs. inactive speech in noisy speech is an important part of speech applications, typically in order to achieve a lower bit-rate. In this work, the error rates for raw classification (i.e. with no hangover mechanism) of noisy speech obtained with traditional classification algorithms are compared to the rates obtained with Neural Network classifiers, trained w...

O n 30 September and 1 October this year a conference on ‘‘Ethics, Science and Moral Philosophy of Assisted Human Reproduction’’ was held at the Royal Society in London. The conference was organised by the German philosopher Edgar Dahl and the eminent embryologist Robert Edwards, and the speakers included scientists, IVF practitioners, and philosophers from the UK, the USA, Europe, and Australi...

The use of present day speech recognition techniques in many practical applications has demonstrated the need for improved algorithm formulation under varying acoustical environments. This paper describes a low-vocabulary speech recognition algorithm which provides robust performance in noisy environments with particular emphasis on characteristics due to Lombard eeect. A neutral and stressed b...

Using deep neural networks (DNNs) for automatic speech recognition (ASR) has recently attracted much attention due to the large performance improvement they provide for a variety of tasks. DNNs are known to be robust to overfitting and to be able to remove speaker variability. Another important cause of variability in speech is the presence of noise. A lot of research has been undertaken on noi...