- Sebastian Stüker, A. Waibel. 2009. Porting Speech Recognition Systems to New Languages Supported by Articulatory Feature Models. Abstract: Linguists estimate the number of currently existing languages to be between 5,000 and 7,000. In order to be able to cover as many languages as possible, techniques have to be developed in order to rapidly port speech recognition systems to new languages in a cost efficient way. In the past, phoneme based, language independent acoustic models have been studied for bootstrapping an acoustic model in a new language. These language independent models usually have seen multiple languages during training, and work under the assumption that phonemes are pronounced the same across languages. Similarly, models for acoustic features, describing the articulator targets of the different phonemes, can also be accurately recognized across languages and can be trained to become language independent in the same way as phonemes can. In the past we combined them with phoneme based models and their behavior on the training languages of the multilingual models was examined. In this paper we present experiments examining the suitability of monolingual and multilingual acoustic features for porting speech recognition systems to new languages. We combined them with monolingual and multilingual, phoneme based models in a stream based frame work in order to bootstrap a model in a new language. The results show that the incorporation of models for articulatory features into the porting framework significantly improves the performance when porting ASR systems to new languages, reducing the word error rate by up to 4.5% relative.
