In most traditional automatic speech recognition (ASR) systems, different languages or dialects are treated separately, and an acoustic model (AM) is typically trained from scratch for each language. This approach leads to several challenges. First, training an AM from scratch requires a large amount of manually labeled data, which is not only expensive but also time-consuming to collect. As a result, there are significant disparities in the quality of acoustic models between languages with abundant data and those with limited resources. For low-resource languages, only small, less complex models can be trained. Moreover, the lack of sufficient labeled data becomes a major bottleneck for emerging or less commonly spoken languages that struggle to gather representative corpora.
Second, training separate acoustic models for each language increases the overall training time, especially when using deep neural networks (DNNs). As discussed in Chapter 7, DNNs are slower to train than Gaussian mixture models (GMMs) due to their larger parameter space and the backpropagation algorithm used. Third, building individual language models for each language complicates the process of recognizing mixed-language speech, making it more costly and less efficient. To address these issues, researchers have turned to multilingual ASR systems that aim to train accurate acoustic models for many languages efficiently while supporting the recognition of mixed-language speech—a crucial scenario in places like Hong Kong, where English and Chinese often mix.
Although resource constraints—such as limited labeled data and computational power—are practical reasons for studying multilingual ASR, they are not the only motivations. Investigating and developing these technologies can also deepen our understanding of algorithms and the relationships between different languages. A significant body of research exists on multilingual and cross-language ASR, including studies such as [265, 431]. In this chapter, we focus specifically on approaches that utilize neural networks.
We will explore various DNN-based multilingual ASR systems. These systems share a common idea: the hidden layer of a DNN can be viewed as a stack of feature extractors, while the output layer directly corresponds to the classification task. These feature extractors can be shared across multiple languages, allowing for joint training and adaptation to new, resource-poor languages. By transferring the shared hidden layer to a new language, the need for extensive retraining is reduced, as only the output layer needs to be fine-tuned.
One early approach to multilingual ASR involved Tandem and bottleneck features. These methods were widely used before the rise of DNN-HMM hybrid systems. Neural networks were used to classify phonetic states, and their outputs or hidden layers served as features for GMM-HMM models. The shared nature of phonemes across languages made it possible to transfer these features from one language to another, especially when the target language had limited data.
Another approach is the multi-language deep neural network that shares the hidden layer. This structure allows for simultaneous training of all languages, improving performance through shared feature learning. The shared hidden layer acts as a generic feature extractor, while each language has its own output layer. This setup supports efficient training and adaptation, even when data is scarce.
Cross-language model migration is another promising technique. By extracting the shared hidden layer from a multilingual DNN and adding a new output layer, it's possible to adapt the model to a new language with minimal data. Experiments have shown that this approach can significantly reduce word error rates, even when the source and target languages are quite different.
Overall, multilingual ASR offers a powerful way to overcome the limitations of traditional single-language systems, enabling more efficient and scalable speech recognition across diverse linguistic environments.
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