Hybrid system for robust recognition of noisy speech based on evolving fuzzy neural networks and adaptive filtering

Speech and signal processing technologies need new methods that deal with the problems of noise and adaptation in order for these technologies to become common tools for communication and information processing. This paper is concerned with a method and a system for adaptive speech recognition in a noisy environment (ASN). A system based on the described method can store words and phrases spoken by the user and subsequently recognize them when they are pronounced as connected words in a noisy environment. The method guarantees system robustness in respect to noise, regardless of its origin and level. New words, pronunciations, and languages can be introduced to the system in an incremental, adaptive mode. The method and system are based on novel techniques recently created by the authors, namely: adaptive noise suppression, and evolving connectionist systems. Potential applications are numerous, e.g. voice dialling in a noisy environment, voice command control, improved wireless communications, data entry into databases, helping disabled people, multimedia systems, improved human computer interaction. The method and system are illustrated on the recognition of English and Italian spoken digits in different noisy environments.