Data Products

List of Speech-to-Text Papers:

• "End-to-End Speech-to-Text Translation with Knowledge Distillation" - This paper proposes a method for improving speech-to-text translation by using knowledge distillation from a pre-trained machine translation model.
• "Speech-to-Text Translation with Transformer Models" - This paper explores the use of transformer models for speech-to-text translation and compares their performance to traditional recurrent neural network models.
• "Unsupervised Speech-to-Text Translation" - This paper presents a method for unsupervised speech-to-text translation, which does not require parallel data for training.
• "Adversarial Training for Robust Speech-to-Text Translation" - This paper proposes an adversarial training approach to improve the robustness of speech-to-text translation models to noise and other distortions.

Summary and Context:

Speech-to-text technology has become increasingly important in recent years, as more and more people rely on voice assistants and other speech-based interfaces to interact with technology. These papers explore various approaches to improving speech-to-text translation, including the use of transformer models, unsupervised learning, and adversarial training. By improving the accuracy and robustness of speech-to-text translation, these approaches have the potential to make speech-based interfaces more reliable and user-friendly.

Survey on Speech-to-Text Technology:

A survey conducted by Voicebot.ai in 2020 found that 55% of US adults use voice assistants on a regular basis, and that 72% of those users use them on a daily basis. The survey also found that the most common use cases for voice assistants are playing music, getting weather updates, and setting reminders. However, the survey also found that many users are still hesitant to use voice assistants for more complex tasks, such as making purchases or controlling smart home devices.