Crosslingual and Multilingual Speech Recognition Based on the Speech Manifold

Speech signals are produced by the smooth and continuous movements of the human articulators. An articulatory representation of speech is considered to be a more compact, more universal, and language-independent speech feature space and can, therefore, improve crosslingual and multilingual speech recognition systems, especially when porting components from one language to another in low-resource scenarios. However, learning the acoustic-to-articulatory conversion has proven to be a very challenging task. In this paper, we utilize a manifold learning technique to derive a nonlinear feature transformation from the conventional filterbank feature space to an articulatory-like feature space. The coordinates in the resultant representation of which some have demonstrable phonological meaning are shown to be highly portable across languages. We propose a proper framework in terms of data selection and graph construction to train coordinates from multilingual data, which allows for training the coordinate space when we have abundant out-of-language data. Deep neural network (DNN) bottleneck features are demonstrated to exhibit a greater degree of language independence when using this representation than in the case of filterbank features as inputs. The usability of this representation is further demonstrated in a number of speech recognition experiments using DNNs in a variety of crosslingual and multilingual scenarios using the multilingual GlobalPhone dataset. Especially, speech recognition systems developed in low-resource settings profit from the improved portability across languages.