Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved recognition of multiple languages in audio data. One method including: receiving a trained split head multilingual neural network model, the trained split head multilingual neural network model including shared acoustic model layers and a plurality of projection layers, each projection layer of the plurality of projection layers corresponding to a language that the trained split head multilingual neural network model recognizes; receiving audio data, the audio data including speech in a plurality of languages in the audio data, the speech in the plurality of languages corresponding the language recognized by a projection layer of the plurality of projection layers of the trained split head multilingual neural network model; and classifying one or more languages of the speech of the audio data using the trained split head multilingual neural network model.

Abstract: An automatic speech recognition (ASR) system recognizes speech expressed in different languages. The ASR system includes a language-agnostic encoding component and prediction component. A language-specific joint analysis system generates first-language probabilities for symbols of a first language and second-language probabilities for symbols of a second language, based on outputs generated by the encoding component and the prediction component. The ASR system then modifies the probabilities produced by the joint analysis system by language-specific weighting information that, in turn, is produced by an attention system. This yields modified first-language probabilities and modified second-language probabilities. Finally, the ASR system predicts an updated instance of label information based on the modified first-language probabilities and the modified second-language probabilities. The ASR system can be successfully applied to recognize an utterance that combines words in two or more languages.