- "Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers."
Converting spoken language into written form.
Phonetics: The study of the sounds used in languages and how they are produced by the human vocal system.
Acoustics: The branch of physics concerned with the properties of sound waves and how they move through different media.
Signal Processing: The use of mathematical and computational algorithms to analyze and manipulate audio waveforms.
Pattern Recognition: The ability to identify and categorize complex patterns of information, such as those found in spoken language.
Machine Learning: The use of computer algorithms to learn from data, recognize patterns and make predictions.
Natural Language Processing: The ability of machines to understand, interpret and generate human language.
Linguistics: The scientific study of language and its structure, including phonology, morphology, syntax and semantics.
Artificial Intelligence: The creation of machines that can perform tasks that typically require human intelligence, such as recognizing speech or language.
Voice User Interface (VUI) Design: The process of creating user-friendly and effective interfaces for voice-controlled devices.
Speech Recognition Systems Architecture: The design and implementation of systems for recognizing and interpreting spoken language.
Speech-to-Text (STT): The process of converting spoken language into written text.
Text-to-Speech (TTS): The process of generating spoken language from written text.
Language Modeling: The development of statistical models to predict the likelihood of spoken or written language.
Deep Learning: A subset of machine learning that involves training artificial neural networks with multiple layers of data processing.
Speech Analysis: The process of using various techniques to analyze and understand the features of spoken language, including pitch, volume, tone and formant frequencies.
Speaker Recognition: The ability to identify the individual speaker based on their voice characteristics.
Conversational Agents: AI-driven chatbots or voice assistants that can have natural conversations with humans.
Automatic Speech Recognition (ASR): The process of recognizing and transcribing spoken language automatically.
Quality Evaluation: The process of measuring the effectiveness and accuracy of speech recognition systems.
Speech Corpora: A large collection of audio recordings and transcripts used for developing and testing speech recognition systems.
Command and control: This type of speech recognition is used for simple statements and commands that the system can easily recognize and act upon, such as turning on a light or setting an alarm.
Dictation: This type of speech recognition is used for transcribing spoken words into text, such as for writing documents or composing emails.
Conversational: This type of speech recognition is capable of understanding and responding to natural language conversations, allowing for more complex interactions with users.
Speaker identification: This type of speech recognition is used to verify a speaker's identity by analyzing their voice characteristics, such as pitch and tone.
Speaker diarization: This type of speech recognition is used to distinguish between different speakers in a conversation, enabling more accurate transcription and analysis of the conversation.
Speech-to-text: This type of speech recognition is used to convert spoken words into written text, which can then be analyzed or stored for later use.
Text-to-speech: This type of speech recognition is used to convert written text into spoken words, making it possible for computer systems to interact with users using natural language.
Speech analytics: This type of speech recognition is used to analyze and interpret spoken language, allowing businesses to gain insights into customer feedback or interactions with their products or services.
Emotion detection: This type of speech recognition is used to analyze the emotional content of spoken language, allowing for more personalized interactions with users based on their emotional state.
Linguistic profiling: This type of speech recognition is used to analyze the language patterns and word choices of a speaker, providing insights into their background, education, and social or cultural influences.
- "It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT)."
- "It incorporates knowledge and research in the computer science, linguistics, and computer engineering fields."
- "The reverse process is speech synthesis."
- "Some speech recognition systems require 'solly' (also called 'enrollment') where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy."
- "Systems that do not use training are called 'speaker-independent' systems."
- "Systems that use training are called 'speaker-dependent'."
- "Speech recognition applications include voice user interfaces such as voice dialing, call routing, domotic appliance control, search keywords, simple data entry, preparation of structured documents, determining speaker characteristics, speech-to-text processing, and aircraft (usually termed direct voice input)."
- "The term voice recognition or speaker identification refers to identifying the speaker, rather than what they are saying."
- "Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person's voice or it can be used to authenticate or verify the identity of a speaker as part of a security process."
- "From the technology perspective, speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data."
- "The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems."