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As Bush and Terry (1997) point out, from ‘curricular objectives to lesson planning … from teacher training to software applicability, there will be no aspect of foreign language learning that will not be influenced by the technological revolution’ (p. xiv). Given the overall advantage of technology-enhanced language learning, many software/tools are developed to facilitate English learning and teaching. Some of them are briefly reviewed in the following, from the perspective of general English language

learning, pronunciation learning and teaching, listening comprehension, and speech analysis. Audio tools developed in China are also discussed below.

A. General English language learning tools

Tell Me More is one of the global online multi-lingual language learning programmes,

which covers not only the training of essential skills in listening, speaking, reading and writing, but also vocabulary, grammar and culture. Its apparent advantage is that it provides a systematic English learning syllabus and makes English learning engaging and interactive. However, its drawback is also widely acknowledged: that it does not really provide any meaningful feedback on the pronunciation accuracy of language users, even though graphical wave forms are presented.

B. Pronunciation learning and teaching tools

Apart from Tell Me More, there are also some other language learning tools, especially those on pronunciation learning and teaching, which integrate automatic speech recognition (ASR) technology in the learning system. The reliability of ASR technology is investigated by Kim (2006), via FluSpeak, software for learning and teaching pronunciation. The results show that the overall accuracy of recognition is still not as accurate as human analysis, in which the correlation coefficient at word level is not high and near zero for intonation level (p.330). This implies that automatic speech recognition software may be only useful for learners to practise some aspects of pronunciation. This, as argued by Bacalu and Delmonte (1999), in general seems to be a shortcoming in most pronunciation-recognition tools, for example, PLASER and

A pronunciation scoring algorithm, presented by Witt and Young (1998), is shown to calculate accurately a language user’s pronunciation at phonemic level, in which both individual mispronunciations are detected and a general assessment of which sounds tend to be mispronounced are indicated as well. However, it cannot be used as an independent learning tool, but has to be embedded within an interactive language teaching system containing modules for, inter alia, error analysis, pronunciation feedback and assessment.

There are also some pronunciation learning tools specifically focusing on English vowel training. Electronic visual feedback, investigated by Lambacher (2001), is a computer- assisted training tool for accent reduction. The deviation between the acoustic features of the master’s production and the learner’s are displayed visually, in which the real- time evaluation and assessments of learner’s mistakes and progress can be provided. As it is not devised originally for language learning, the interface, therefore, is not really user-friendly. It is often too complicated for a user to understand and interpret, and a basic knowledge of acoustic phonetics is needed. This is also the drawback for the development undertaken by Brett (2004) on an application of the PRAAT programme to calculate and plot the formants on a graph to provide learners with real time feedback on their vowel production.

C. Listening comprehension tools

Apart from the general English learning tool and those specifically concentrating on pronunciation learning and teaching, there are also some tools more interested in helping learners to improve their listening skills. Electronic dictionary, produced by Speechinaction, is an efficient pronunciation and listening training system, which makes

fast streaming speech easily understandable by exposing language learners to various possible phonetic environments of an utterance. The advantage of this programme is that, by being exposed to different delivery speeds, language learners are given the chance to perceive various phonetic changes of a sound in which some linguistic characteristics can be compared, e.g., articulation, sound reduction, so as to facilitate the understanding of natural L1 English speech. However, the productions at different speeds are mainly spoken by means of artificially adjusting them to fit into different speed bands. These productions are not natural spontaneous speech, which inevitably weakens its advantage of exposure to different phonetic environments.

D. Speech analysis tools

In addition to the technologies mentioned above used for language learning, there are other speech analysis toolsets which can also facilitate language learners in advancing their linguistic abilities. For example, The Speech Analyzer produced by SIL can provide detailed analysis on waveform, pitch, intensity and spectrogram. However, this speech analysis tool might not easily be used by language learners due to the fact that they are aimed at researchers and are technically sophisticated.

E. Audio tools developed in China

In China, there are also some products developed by Chinese researchers for English learning and teaching. For example, StepbyStep is one of those tools widely aimed at primary and secondary school students for pronunciation and listening training, in which language learners can adjust five different bands of delivery speeds from very slow through to very fast. However, this kind of speed change is not really helpful, since the slow-down is not linear.

Given this brief review of some representative English learning and teaching tools, it is not difficult to see that these TELL systems are useful to some extent in helping language learners enhance their English learning. Their potential drawbacks, however, impede language learners in further progressing their communicative competence.