Neural Language Codes for Multilingual Acoustic Models
This addresses the problem of costly and inferior adaptive methods in multilingual speech recognition for AI applications, offering a novel modulation approach.
The paper tackles the high cost of multilingual speech recognition by proposing a Meta-Pi network that modulates a large multilingual acoustic model with language-specific codes, enabling quick adaptation without retraining or new data and outperforming monolingual models.
Multilingual Speech Recognition is one of the most costly AI problems, because each language (7,000+) and even different accents require their own acoustic models to obtain best recognition performance. Even though they all use the same phoneme symbols, each language and accent imposes its own coloring or "twang". Many adaptive approaches have been proposed, but they require further training, additional data and generally are inferior to monolingually trained models. In this paper, we propose a different approach that uses a large multilingual model that is \emph{modulated} by the codes generated by an ancillary network that learns to code useful differences between the "twangs" or human language. We use Meta-Pi networks to have one network (the language code net) gate the activity of neurons in another (the acoustic model nets). Our results show that during recognition multilingual Meta-Pi networks quickly adapt to the proper language coloring without retraining or new data, and perform better than monolingually trained networks. The model was evaluated by training acoustic modeling nets and modulating language code nets jointly and optimize them for best recognition performance.