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Analysis of the traditional automatic speech recognition (ASR) system for Google applications
Currently, Google's voice search applications still rely on traditional automatic speech recognition (ASR) systems. These systems consist of three main components: an acoustic model (AM), a pronunciation model (PM), and a language model (LM). Each of these models is trained independently, requiring researchers to manually fine-tune them across different datasets. For instance, when the acoustic model captures certain acoustic features, it may refer to phonemes in context or even unrelated ones, generating a series of subword unit predictions. The pronunciation model then maps these phonemes using a hand-crafted dictionary, while the language model assigns words based on the probability of the sequence.
This independent training approach is actually suboptimal compared to joint training, which could lead to a more efficient and accurate system. In recent years, end-to-end systems have gained popularity. They aim to integrate all components into a single model that learns together, but despite promising results in research papers, there's still no clear evidence that they outperform traditional systems in real-world scenarios.
To test this, Google recently published a paper titled "State-of-the-Art Speech Recognition With Sequence-to-Sequence Models" from the Google Brain Team. This paper introduces a new end-to-end speech recognition model that shows significant improvements. Compared to existing tools, Google’s model achieves a word error rate (WER) of 5.6%, a 16% improvement over the previous 6.7%. Additionally, the model doesn’t require separate language or pronunciation models, making it much smaller—only one-eighth the size of traditional systems.
The model is built on the Listen-Attend-Spell (LAS) architecture, which consists of three parts: an encoder, an attention module, and a decoder. The encoder processes the input audio signal and transforms it into a high-level representation. The attention module aligns the input with the predicted subword units, and the decoder generates the final word sequence, similar to a language model.
Unlike traditional systems, all components of LAS are jointly trained within a single neural network, simplifying the overall process. It also eliminates the need for external components like finite state transducers, lexicons, or text normalization models. Furthermore, it doesn't require time alignment or decision trees during training, as it can be directly trained on paired audio and text data.
In the paper, the Google Brain team introduced several innovations, such as improving the attention mechanism and using longer subword units like WordPiece. They also employed optimized training methods, including training with minimal word error rates. These advancements contributed to the 16% performance gain over traditional models.
Another exciting aspect is the model’s ability to support multiple dialects and languages. Since it’s a unified neural network, it can be trained on diverse data without needing separate AM, PM, and LM components for each language. Testing showed that the model performed well on 7 English dialects and 9 Indian languages, outperforming traditional models.
However, the model is not yet fully mature. It struggles with real-time processing, which is crucial for voice search applications. There's also a gap between the training data and real-world usage—Google’s model was trained on only 22,000 audio-text conversations, far fewer than traditional systems. Rare words, such as proper nouns or technical terms, are often misrecognized. Therefore, while the model shows great promise, there are still many challenges to overcome before it becomes widely practical.