In WERNICKE, on top of substantial theoretical results, it was demonstrated, using standard international reference databases (such as the unlimited vocabulary ARPA North American Business News database, and the EU funded SQALE project), that the hybrid HMM/ANN approaches lead to competitive state-of-the-art speech recognizers. Furthermore, the investigated hybrid approach was shown to have additional advantages in terms of CPU utilisation and memory bandwidth. These conclusions have been confirmed by many different independent sources.
While building on the WERNICKE large vocabulary continuous Speech Recognition system, SPRACH will also investigate the development of flexible systems for smaller, task independent applications, in different languages (UK English, French and Portuguese).
The industrial relevance of this project is high, and many useful results are expected. Firstly, it is clear that speech processing, and Speech Recognition in particular, will play a major role in the future multimedia and telematics applications. In this respect, while SPRACH is fully exploiting the promising HMM/ANN technology, it also addresses most of the relevant issues of Speech Recognition in general, such as language and lexicon modelling, application domain adaptation, and prototype development. Secondly, on top of its obvious relevance to the speech recognition technology, it is also important to note that, motivated by the results achieved in WERNICKE, these hybrid systems have already been adopted by several industries and laboratories in many different areas. To reinforce the industrial relevance of this project and its possible industrial impact, a SPRACH Industrial Advisory Board including BBC (UK), CSELT (I), Daimler-Benz (D) and Thomson (F) has been set up.
Possible applications and demonstration systems that will be targeted in this project include, e.g.:
- Very large vocabulary (>64K words) continuous speech recognition of read speech (multimedia applications).
- Voice-driven typewriter with simple editing commands.
- Flexible continuous speech recognizer in which lexica and grammars can be defined on the spot, without the need of training.
- Application to smaller (but realistic) tasks, including, e.g., robust recognition of free format numbers.
- Recognition of broadcast speech-transcription of radio or television speech (e.g. news readers).
- Languages: US English, UK English, French and Portuguese.
The goal of the proposed project is to further improve the current state-of-the-art in continuous Speech Recognition using Artificial Neural Network (ANN) and Hidden Markov Model (HMM) approaches. Pursuing the theoretical and development work successfully carried out under the WERNICKE project (ESPRIT Basic Research Project 6487, October 1992-October 1995), this new project, referred to as SPRACH (Speech Recognition Algorithms for Connectionist Hybrids), will extend the research to robust and flexible Speech Recognition systems that can easily be adapted to new languages and new domains with new lexica and new syntaxes.
Funding SchemeCSC - Cost-sharing contracts
CB2 1TN Cambridge
S10 2TN Sheffield