Joint Estimation of Articulatory Features and Acoustic Models for Low-Resource Languages

Using articulatory features for speech recognition improves the performance of low-resource languages. One way to obtain articulatory features is by using an articulatory classifier (pseudo-articulatory features). The performance of the articulatory features depends on the efficacy of this classifier. But, training such a robust classifier for a low-resource language is constrained due to the limited amount of training data. We can overcome this by training the articulatory classifier using a high resource language. This classifier can then be used to generate articulatory features for the low-resource language. However, this technique fails when high and low-resource languages have mismatches in their environmental conditions. In this paper, we address both the aforementioned problems by jointly estimating the articulatory features and low-resource acoustic model. The experiments were performed on two low-resource Indian languages namely, Hindi and Tamil. English was used as the high-resource language. A relative improvement of 23% and 10% were obtained for Hindi and Tamil, respectively.