Discovery of Words: towards a Computational Model of Language Acquisition

Abstract

Human speech recognition seems effortless, but so far it has been impossible to approach human performance by machines. Compared with human speech recognition (HSR), the error rates of state-of-the-art automatic speech recognition (ASR) systems are an order of magnitude larger (Lee, 2004; Moore, 2003; see also Scharenborg et al., 2005). This is true for many different speech recognition tasks in noise-free environments, but also (and especially) in noisy environments (Lippmann, 1997; Sroka & Braida, 2005; Wesker et al., 2005). The advantage for humans remains even in experiments that deprive humans from exploiting ‘semantic knowledge’ or ‘knowledge of the world’ that is not readily accessible for machines. It is well known that there are several recognition tasks in which machines outperform humans, such as the recognition of license plates or barcodes. Speech differs from license plates and bar codes in many respects, all of which help to make speech recognition by humans a fundamentally different skill. Probably the most important difference is that bar codes have been designed on purpose with machine recognition in mind, while speech as a medium for human-human communication has evolved over many millennia. Linguists have designed powerful tools for analyzing and describing speech, but we hardly begin to understand how humans process speech. Recent research suggests that conventional linguistic frameworks, which represent speech as a sequence of sounds, which in their turn can be represented by discrete symbols, fail to capture essential aspects of speech signals and, perhaps more importantly, of the neural processes involved in human speech understanding. All existing ASR systems are tributable to the beads-on-a-string representation (Ostendorf, 1999) invented by linguistics. But is quite possible –and some would say quite likely- that human speech understanding in not based on neural processes that map dynamically changing signals onto sequences of discrete symbols. Rather, it may well be that infants develop very different representations of speech during their language acquisition process. Language acquisition is a side effect of purposeful interaction between infants and their environment: infants learn to understand and respond to speech because it helps to fulfil a set of basic goals (Maslow, 1954; Wang, 2003). An extremely important need is being able to adapt to new situations (speakers, acoustic environments, words, etc.) Pattern recognisers, on the other hand, do not aim at the optimisation of ‘purposeful