Description of multi-language and cross-language speech recognition technology

In most traditional automatic speech recognition (ASR) systems, different languages or dialects are treated independently, with an acoustic model (AM) typically trained from scratch for each language. This approach leads to several challenges. First, training a model for a language from the ground up requires a large amount of manually labeled data, which is both expensive and time-consuming to collect. As a result, there's a significant disparity in the quality of acoustic models between languages with abundant resources and those with limited data. For low-resource languages, only small, less complex models can be developed. Additionally, the lack of representative corpora makes it difficult to build effective models for new or less common languages. Second, training separate AMs for each language increases the overall training time, especially in deep neural network (DNN)-based systems where training is slower due to the high number of parameters and the backpropagation algorithm used in Gaussian mixture models (GMM). Third, creating individual language models for each language complicates multilingual recognition and raises the cost of handling mixed-language speech. To address these issues, researchers are increasingly focusing on developing multilingual ASR systems that can efficiently train accurate acoustic models across many languages, reduce training costs, and support seamless recognition of mixed-language speech—such as when English words are inserted into Chinese phrases in Hong Kong. Although resource constraints like limited labeled data and computational power drive research into multilingual ASR, this is not the only motivation. Studying and implementing such systems also enhances our understanding of algorithms and the relationships between languages. Multilingual and cross-lingual ASR has been widely explored, but this chapter focuses specifically on approaches using neural networks. We will explore various DNN-based multilingual ASR systems. These systems share a core idea: the hidden layer of a DNN acts as a feature extractor, while the output layer corresponds directly to the target classification. These features can be shared across multiple languages, trained together, and adapted to new ones. By transferring the shared hidden layer to a new language, we can reduce the required data without retraining the entire network, as only the output layer needs to be adjusted. One early method involved Tandem and bottleneck features, where neural networks were used to classify phonetic states, and their outputs served as features for GMM-HMM models. These features could be transferred from one language to another, improving performance for low-resource languages. However, this approach was rarely used for full multilingual systems because each language still needed its own GMM-HMM system unless they shared similar phonemes or decision trees. Another approach is the multi-language DNN sharing a hidden layer, known as SHL-MDNN. In this structure, the input and hidden layers are shared among all languages, while each language has its own softmax output layer. This allows for efficient training and adaptation. The shared hidden layer functions as a generic feature transform, and the system benefits from multitasking by learning features that generalize across tasks. Training SHL-MDNN effectively requires simultaneous training on all languages. While batch methods like L-BFGS make this straightforward, smaller batches (e.g., SGD) require careful data mixing. An alternative method involves pre-training with unsupervised techniques and fine-tuning per language, though this may lead to suboptimal results compared to joint training. Experiments show that SHL-MDNN improves recognition accuracy across multiple languages. Microsoft’s internal evaluation demonstrated a 3–5% reduction in word error rate (WER) compared to single-language DNNs. Moreover, adding a new language to SHL-MDNN simply involves adding a new softmax layer and training it with the new data. Cross-language model migration further enhances performance. By borrowing the shared hidden layer from a multilingual DNN and training a new softmax layer on the target language, we can achieve significant improvements. Experiments showed that even with minimal target data, the WER could be reduced by up to 28%. This technique is particularly useful for languages far from the source, such as Mandarin Chinese, where it saved over 100 hours of annotation work. Finally, while unsupervised learning can help, labeled data remains crucial for efficient feature learning. Results showed that using labeled data led to much greater improvements than relying solely on unlabeled data. Thus, while unsupervised methods offer convenience, they cannot fully replace the value of annotated datasets in multilingual ASR.

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