The ASR Blog
Emerging research, state-of-the-art methods, and practical insights for building robust speech recognition systems.
Current state of the art ASR system has outperformed conventional ASR system. The performance of deep neural networks in ASR has reached to professional human transcribers in clean speech environment conditions. However, it has been affected by the following challenges:
In this section; we will highlight emerging and state of the art methods used for building a robust speech recognition system from research point of view. e.g we will explain how can we address the challenges occurred in speech recognition as mentioned above.
How deep learning-based speech enhancement preprocessing improves accuracy in noisy real-world recordings.
Techniques for handling speakers who naturally switch between English, Urdu, and Hindi within a single utterance.
Why standard models fail on non-adult speech and what training data strategies improve recognition rates.
Fine-tuning pre-trained transformer models on domain-specific corpora for legal, medical, and technical transcription.
Comparing CTC, attention-based encoder-decoders, and transducer models for production-grade speech recognition.
The future of ASR: self-supervised learning approaches that reduce the need for costly labelled transcription data.