Language Identification and Multilingual Speech Recognition Using Discriminatively Trained Acoustic Models

Overview The paper performs language identification experiments for four prominent South-African languages using a multilingual speech recognition system. Specifically, the paper shows how successfully Afrikaans, English, Xhosa and Zulu may be identified using a single set of HMMs and a single recognition pass. The paper further demonstrates the effect of language identification-specific discriminative acoustic model training on both the per-language recognition accuracy as well as the accuracy of the language identification process. Experiments indicate that discriminative training leads to a small overall improvement in language identification accuracy while not affecting the speech recognition performance strongly. Furthermore, language identification is found to be more error prone and discriminative training less effective for code-mixed utterances, indicating that these may require special treatment within a multilingual speech recognition system.