- "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."
Speech analysis, synthesis, coding, and recognition, applications in speech-based human-machine interaction and automatic speech recognition systems.
Digital Signal Processing (DSP): The mathematical analysis, manipulation, and transformation of signals that are represented as sequences of numbers.
Fourier Analysis: A mathematical technique that breaks down a signal into its constituent frequencies, allowing for a better understanding of how the signal is composed.
Probability and Statistics: The study of the likelihood of events and the examination of data using statistical methods.
Linear Algebra: The branch of mathematics that deals with vector spaces and linear transformations, which are fundamental in the representation and manipulation of signals.
Time and Frequency Domain Analysis: The study of signals in either the time domain or the frequency domain, which are two different ways of analyzing signals.
Filtering Techniques: The use of filters to modify the frequency content of a signal, which is commonly used in speech processing for noise reduction and speech enhancement.
Feature Extraction Techniques: The process of selecting representative features from a signal, which are commonly used in speech recognition to identify the characteristics of speech sounds.
Hidden Markov Models (HMMs): A statistical model that is widely used in speech recognition to represent an unknown sequence of observations with a sequence of states that are hidden from view.
Machine Learning: The application of statistical algorithms that automatically improve performance on a specific task through experience.
Pattern Recognition: The recognition of patterns in data, which is essential in speech processing to identify speech sounds and language patterns.
Neural Networks: A type of machine learning algorithm that simulates the behavior of the human brain, which has been successful in speech processing tasks such as speech recognition and synthesis.
Acoustic Modeling: The process of characterizing the properties of sound waves that are produced by a particular speaker or environment, which is essential in speech recognition.
Electronic Speech Analysis: Analysis of the characteristics of speech signals such as vocal tract models, spectral analysis, and phonetic analysis.
Speech Syntactic Analysis: The study of the sentence structure of natural languages such as speech tagging, parsing and language modeling.
Linguistics: The study of language and its structure, which is important for developing speech recognition systems for multiple languages.
Speech Coding: The process of encoding speech into a digital signal for transmission or storage.
Speech Enhancement: The process of improving the quality of speech by reducing noise and distortion.
Speech Recognition: The process of converting spoken words into text or commands for a computer system.
Speech Synthesis: The process of converting text into spoken words using a computer-generated voice.
Speaker Verification: The process of verifying the identity of a speaker by analyzing their voice.
Language Identification: The process of determining the language being spoken by analyzing the audio signal.
Speaker Diarization: The process of separating multiple speakers in an audio recording and identifying each one.
Emotion Recognition: The process of detecting and analyzing emotions in spoken language.
Prosody Analysis: The analysis of speech patterns and intonation, including stress, rhythm, and pitch.
Voice Activity Detection: The process of detecting and filtering out non-speech sounds from an audio signal.
Speech Segmentation: The process of dividing a continuous speech signal into smaller units for analysis.
Speech-to-Text Alignment: The process of aligning speech to text for use in transcription, subtitling, and translation.
Speech Diagnostics: The process of diagnosing speech disorders and abnormalities using speech processing tools.
Speaker Adaptation: The process of customizing speech recognition and synthesis systems to a specific user's voice and speech patterns.
Speech Translation: The process of translating spoken language from one language to another in real-time.
- "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."