55 AI BASICS Speech Recognition

Sinsavk AI for beginners · Beginner ·📐 ML Fundamentals ·4mo ago

About this lesson

Link to my YT channel SINSAVK AI FOR BEGINNERS https://www.youtube.com/channel/UCWYy-VfH3A92kS4HNWZXsMA Speech recognition is one of the most widely recognized applications of artificial intelligence, allowing machines to convert spoken language into written text or actionable commands. This technology is at the heart of virtual assistants like Siri, Alexa, and Google Assistant, voice-controlled devices, transcription services, and many other applications where human speech needs to be interpreted and understood by computers. Speech recognition combines multiple AI techniques, including signal processing, natural language processing, and machine learning, to accurately capture and interpret human speech. The process begins with audio input, which is usually a continuous waveform representing the sound of a person speaking. Before the AI can understand it, this audio signal needs to be processed and transformed into a format suitable for machine learning. This involves steps like noise reduction, normalization, and feature extraction. One common approach is to convert the raw audio into spectrograms, which visually represent the frequency components of the sound over time. These features make it easier for AI models to detect patterns in speech, such as phonemes, syllables, and words. Modern speech recognition systems rely heavily on deep learning, particularly neural networks. Recurrent neural networks, including long short-term memory networks, were historically popular because they can model sequential data and capture temporal dependencies in speech. More recently, transformer-based architectures have become standard due to their ability to handle long-range dependencies efficiently and process large amounts of data in parallel. These models are trained on massive datasets containing hours of spoken audio paired with corresponding transcriptions, enabling them to learn both the acoustic patterns of speech and the linguistic structure of language. Once the audio

Original Description

Link to my YT channel SINSAVK AI FOR BEGINNERS https://www.youtube.com/channel/UCWYy-VfH3A92kS4HNWZXsMA Speech recognition is one of the most widely recognized applications of artificial intelligence, allowing machines to convert spoken language into written text or actionable commands. This technology is at the heart of virtual assistants like Siri, Alexa, and Google Assistant, voice-controlled devices, transcription services, and many other applications where human speech needs to be interpreted and understood by computers. Speech recognition combines multiple AI techniques, including signal processing, natural language processing, and machine learning, to accurately capture and interpret human speech. The process begins with audio input, which is usually a continuous waveform representing the sound of a person speaking. Before the AI can understand it, this audio signal needs to be processed and transformed into a format suitable for machine learning. This involves steps like noise reduction, normalization, and feature extraction. One common approach is to convert the raw audio into spectrograms, which visually represent the frequency components of the sound over time. These features make it easier for AI models to detect patterns in speech, such as phonemes, syllables, and words. Modern speech recognition systems rely heavily on deep learning, particularly neural networks. Recurrent neural networks, including long short-term memory networks, were historically popular because they can model sequential data and capture temporal dependencies in speech. More recently, transformer-based architectures have become standard due to their ability to handle long-range dependencies efficiently and process large amounts of data in parallel. These models are trained on massive datasets containing hours of spoken audio paired with corresponding transcriptions, enabling them to learn both the acoustic patterns of speech and the linguistic structure of language. Once the audio
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