Language independent and unsupervised acoustic models for speech recognition and keyword spotting

Developing high-performance speech processing systems for low-resource languages is very challenging. One approach to address the lack of resources is to make use of data from mul-tiple languages. A popular direction in recent years is to train a multi-language bottleneck DNN. Language dependent and/or multi-language (all training languages) Tandem acoustic mod-els (AM) are then trained. This work considers a particular scenario where the target language is unseen in multi-language training and has limited language model training data, a limited lexicon, and acoustic training data without transcriptions. A zero acoustic resources case is first described where a multi-language AM is directly applied, as a language independent AM (LIAM), to an unseen language. Secondly, in an unsu-pervised approach a LIAM is used to obtain hypotheses for the target language acoustic data transcriptions which are then used in training a language dependent AM. 3 languages fro