profileImg
ASR
Automatic Speech Recognition
  1. Neural Network-Based ASR
  2. Wav2Vec2.0
  3. Implementing

ASR

Automatic Speech Recognition (ASR) is a technology that empowers computers to interpret and transcribe spoken language into written text. This innovative field, falling under the umbrella of computational linguistics, plays a pivotal role in applications ranging from voice-to-text dictation software to virtual assistants, call center systems, transcription services, and accessibility tools. Additionally, ASR systems can be employed for speech synthesis, generating spoken language from text or other inputs. Types of ASR Systems Rule-Based ASR: Rule-based Automatic Speech Recognition (ASR) employs predefined rules and patterns to match acoustic features with linguistic units, prioritizing simplicity and speed. While effective in well-defined language and low-noise scenarios, it has limitations such as a restricted vocabulary and struggles with noisy or ambiguous speech. Consequently, rule-based ASR excels in specific contexts but may not be ideal for applications demanding flexibility in vocabulary or robust performance in challenging acoustic conditions. Statistical ASR: Statistical Automatic Speech Recognition (ASR) utilizes statistical models to map acoustic features to linguistic units, demonstrating adaptability with variable vocabulary sizes and noise resilience. While versatile, it requires substantial training data and computational resources for accurate models. The statistical approach enables the system to extract intricate patterns from diverse datasets, enhancing its flexibility in transcribing speech. This makes statistical ASR well-suited for applications dealing with varying vocabulary sizes and common challenges like ambient noise. Neural Network-Based ASR: Neural network-based Automatic Speech Recognition (ASR) utilizes deep learning to achieve high accuracy and robust performance in transcribing speech. By discerning intricate patterns in speech signals, it excels in recognizing nuances. However, this heightened performance requires substantial training data and computational resources. The training process involves exposing the network to large, diverse datasets, demanding significant computational power. Despite its resource-intensive nature, neural network-based ASR is a formidable choice for applications prioritizing precision and adaptability in handling diverse linguistic patterns.