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Identifying Language from Raw Speech An Application of Recurrent Neural Networks

People can differentiate spoken languages without understanding them, and, in some sense, this differentiation can only be done without understanding the language. When we consider a multi-lingual person trying to understand an utterance spoken in one of the languages with which they are familiar, they will first decide which language this utterance belongs to, before trying to interpret it. The language identification task is one example of high-level feature abstraction from raw speech. Speech samples are classified into categories according to what language was spoken. We conjecture that this classification can be performed reliably and in real time. To be successful, such a system should be speaker-independent as well as contextindependent. A large amount of training is required to achieve a satisfactory level of performance. We present a continuation of previous work (Kwasny et al., 1992) which introduces two important improvements to the system: (1) replacement of the non-recurrent, feedforward network with a recurrent one, which is smaller, but still classifies correctly; (2) development of a frontend processor on a Next® workstation to facilitate sample recording and data acquisition. This is important for large-scale data acquisition, training, and testing of the network.