Automatic Speech Recognition (ASR) is a technology that converts spoken language into written text. It is also known as Speech-to-Text (STT) conversion. ASR systems analyze audio input and transcribe it into textual output, making it possible to convert spoken words into a format that can be processed, stored, or analyzed by computers.Key components and processes involved in Automatic Speech Recognition include:
Acoustic Model: This part of the system deals with the relationship between audio signals and phonemes (distinct units of sound in a language). It helps in recognizing the sound patterns in speech.
Language Model: The language model helps in predicting the likelihood of word sequences. It considers the context and grammar of a particular language to improve the accuracy of transcription.
Lexicon: The lexicon contains information about the pronunciation of words. It is essential for mapping acoustic features to specific words.
Decoder: The decoder combines information from the acoustic model, language model, and lexicon to generate the most probable transcription of the input speech.
Applications of ASR include:
Voice Commands: ASR is used in voice-activated systems and virtual assistants where users can give commands or ask questions verbally.
Transcription Services: ASR is widely used in services that transcribe spoken words into written text, such as in meeting transcription, dictation, or interview transcription.
Voice Search: ASR powers voice search functionalities in search engines and other applications.
Accessibility: ASR technology is crucial for providing accessibility to individuals with disabilities, allowing them to interact with devices and applications using spoken language.
ASR systems have evolved with advancements in deep learning and neural network technologies, leading to improved accuracy and robustness in recognizing various accents, languages, and speaking styles.