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Whisper-LM: Language Models for Improving Speech Recognition in Low-Resource Languages

Automatic Speech Recognition (ASR) has made enormous strides in recent years, but for languages with limited data resources, the development of accurate models remains a challenge. A promising approach to overcome this hurdle is the integration of Language Models (LMs) into ASR systems. This article highlights the functionality and benefits of Whisper-LM, an innovative approach that leverages the power of language models to improve the accuracy of speech recognition in low-resource languages.

The Challenge of Resource Scarcity

For widely spoken languages like English or Mandarin, huge datasets are available that enable the training of powerful ASR models. However, for less common languages or dialects, the data situation is often sparse. This resource scarcity makes it difficult to train robust models and leads to lower accuracy and limited functionality.

Whisper-LM: A Solution

Whisper-LM builds on the strength of language models to fill the gaps in the training data. Language models are trained to predict the probability of word sequences and can thus provide context information that supports the ASR models in decoding speech signals. By integrating a language model into the ASR system, the accuracy of speech recognition, especially in low-resource languages, can be significantly improved.

How Whisper-LM Works

Whisper-LM combines an acoustic model, which converts speech signals into phonetic representations, with a language model, which evaluates the probability of word sequences based on context. The language model is trained on large text corpora and thus learns the grammatical rules and semantic relationships within the language. During the decoding phase, the ASR system uses the information from the language model to determine the most probable word sequence that corresponds to the acoustic signal. This leads to improved recognition accuracy, especially with noisy or unclear speech signals.

Benefits of Whisper-LM

The integration of language models into ASR systems offers several advantages, especially for low-resource languages:

Improved Accuracy: By using context information, language models can significantly increase recognition accuracy, especially in difficult acoustic conditions.

Robustness to Noise: Language models help the ASR system to recognize the correct word sequence even with noisy speech signals.

Adaptation to Different Accents and Dialects: By training on diverse text data, language models can optimize speech recognition for different accents and dialects.

Reduced Need for Training Data: By using context information, language models can help reduce the need for annotated speech data, which is particularly beneficial for low-resource languages.

Future Prospects

Whisper-LM and similar approaches offer promising possibilities for the further development of speech recognition in low-resource languages. Research is focused on further optimizing the integration of language models and adapting the models to the specific challenges of different languages. The combination of ASR systems with powerful language models opens up new perspectives for the development of speech technologies that are accessible to all languages.

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