Speech Recognition Terms Glossary: Speech Recognition Terms in 2024
A
Acoustic Model
An Acoustic Model is a statistical representation of the relationship between the acoustic features of speech, such as phonemes or spectrums, and the corresponding linguistic units.
Adversarial Examples
Adversarial Examples are inputs intentionally designed to mislead or deceive an AI system, often by adding carefully crafted perturbations to the input data.
Artificial Neural Network (Ann)
An Artificial Neural Network (ANN) is a computational model that loosely simulates the structure and function of biological neural networks.
Artificial Neural Networks (Anns)
Artificial Neural Networks (ANNs) are computational models inspired by the structure and function of biological neural networks, used for solving complex problems.
Asr System
An ASR System is a complete speech recognition system, including components such as acoustic modeling, language modeling, and decoding.
Attention Mechanism
Attention Mechanism is a component in neural network architectures that allows the model to focus on different parts of the input sequence during processing.
Audio Signal Processing
Audio Signal Processing is the manipulation, analysis, and interpretation of audio signals to extract meaningful information or enhance audio quality.
Automatic Speech Recognition (Asr)
Automatic Speech Recognition (ASR) is the process of converting spoken language into written text using speech recognition technology.
B
Backpropagation
Backpropagation is an algorithm used in training artificial neural networks, adjusting the weights and biases based on the error between predicted and target outputs.
Batch Normalization
Batch Normalization is a technique used to improve the training of deep neural networks by normalizing the inputs of each layer.
Beam Search
Beam Search is a search algorithm used in speech recognition to find the most likely sequence of words given a sequence of acoustic features.
Beamforming
Beamforming is a signal processing technique used to enhance the directional sensitivity of a microphone array, focusing on a specific sound source.
C
Confusion Matrix
A Confusion Matrix is a table used to evaluate the performance of a classification model, showing the number of true positive, true negative, false positive, and false negative predictions.
Connectionist Temporal Classification (Ctc)
Connectionist Temporal Classification (CTC) is a technique used for training sequence-to-sequence models, such as speech recognition models.
Context-Dependent Modeling
Context-Dependent Modeling in speech recognition refers to modeling the relationship between phonemes and their acoustic realization, taking into account surrounding phonetic context.
Context-Independent Modeling
Context-Independent Modeling in speech recognition refers to modeling phonemes or speech units without considering their surrounding phonetic context.
Contextual Bandits
Contextual Bandits is a type of reinforcement learning where an agent tries to learn the best action to take in each context, based on past experiences and rewards.
Continuous Speech Recognition
Continuous Speech Recognition is the capability to recognize speech in a continuous stream, often used in real-time transcription or dictation systems.
Convolutional Neural Networks (Cnns)
Convolutional Neural Networks (CNNs) are deep learning models commonly used for image and speech recognition tasks.
D
Data Augmentation
Data Augmentation is a technique used to artificially increase the size of a training set by applying transformations or modifications to the existing data.
Data Preprocessing
Data Preprocessing is the process of cleaning, transforming, and standardizing data before it is used for training a machine learning model.
Decoding
Decoding is the process of mapping the acoustic features of speech to linguistic units or words using a speech recognition system.
Deep Belief Networks (Dbns)
Deep Belief Networks (DBNs) are a class of deep learning models that are based on the hierarchical arrangement of restricted Boltzmann machines.
Deep Learning
Deep Learning is a subfield of AI that uses artificial neural networks with multiple layers to model and understand complex patterns and data representations.
Deep Neural Network (Dnn)
A Deep Neural Network (DNN) is an artificial neural network with multiple hidden layers, used in various speech recognition tasks.
Deep Neural Networks (Dnns)
Deep Neural Networks (DNNs) are a class of artificial neural networks with multiple layers between the input and output layers.
Denoising Autoencoder
A Denoising Autoencoder is a type of artificial neural network used for unsupervised learning that learns to remove noise from input data.
Dictation System
A Dictation System is a speech recognition system specifically designed for converting spoken language into written text.
Distant Speech Recognition
Distant Speech Recognition is the task of recognizing speech that is captured from a distance, such as in a noisy environment or from far-field microphones.
E
Encoder-Decoder Architecture
Encoder-Decoder Architecture is a type of neural network architecture where an encoder processes the input and a decoder generates the output.
End-Point Detection (Epd)
End-Point Detection (EPD) is the task of detecting the start and end points of speech segments in an audio signal, useful in various speech recognition applications.
End-To-End Speech Recognition
End-to-End Speech Recognition is an approach that directly maps the acoustic features of speech to the corresponding textual output, without explicit intermediate steps.
Epoch
In machine learning, an Epoch refers to a complete iteration over the training dataset.
F
F0 (Fundamental Frequency)
F0 or Fundamental Frequency is the lowest frequency in the harmonic series of a periodic sound waveform, corresponding to the perceived pitch of a voice.
Fbank Features
Fbank Features, also known as log filterbank energies, are commonly used features for speech recognition that capture the frequency content of a speech signal.
Feature Extraction
Feature Extraction is the process of selecting relevant and discriminative features from speech signals.
Formants
Formants are the resonant frequencies of the vocal tract that contribute to the quality and timbre of human speech sounds.
G
Gaussian Mixture Model (Gmm)
Gaussian Mixture Model (GMM) is a statistical model used for representing the distribution of acoustic features in speech recognition.
Gmm (Gaussian Mixture Models)
GMM (Gaussian Mixture Models) is a statistical model used to represent the probability distribution of a set of observations, often used in speech recognition for acoustic modeling.
Grammar-Based Recognition
Grammar-Based Recognition is an approach to speech recognition that uses predefined rules or grammar structures to constrain the recognition process.
Grapheme
A Grapheme is the smallest meaningful unit of written symbol in a language.
H
Hidden Markov Model (Hmm)
Hidden Markov Model (HMM) is a statistical model used to represent the probability distribution of a sequence of observable events, where the underlying states generating those events are not directly observable.
Hidden Unit
A Hidden Unit is a node in an artificial neural network that receives inputs and applies a non-linear transformation to compute its output.
K
Keyword Detection
Keyword Detection is the task of identifying specific keywords or phrases in audio data, often used for applications such as voice-controlled devices.
Keyword Extraction
Keyword Extraction is the process of identifying and extracting important words or phrases from a speech or text.
Keyword Spotting
Keyword Spotting is a speech recognition technique that focuses on identifying and recognizing specific keywords or phrases within audio recordings.
Knowledge Distillation
Knowledge Distillation is a technique used to train a smaller, more lightweight model (student) using a larger, more accurate model (teacher) as a guide.
L
Language Identification
Language Identification is the task of determining the language of a given speech or text.
Language Model
A Language Model is a statistical model that predicts the probability of the occurrence of a sequence of words, helping in the interpretation and understanding of speech.
Language Modeling
Language Modeling is the process of estimating the probability of a sequence of words in a given language.
Language Resources
Language Resources are collections of linguistic data, such as text and audio corpora, used for training and evaluating language technologies.
Language Transfer
Language Transfer is the influence of one language on the acquisition or use of another language, often leading to similarities or transfer errors.
Long Short-Term Memory (Lstm)
Long Short-Term Memory (LSTM) is a type of RNN architecture that addresses the vanishing gradient problem.
Lstm (Long Short-Term Memory)
LSTM (Long Short-Term Memory) is a type of recurrent neural network architecture specifically designed to process and model sequential data.
M
Machine Learning
Machine Learning is a field of study that gives computers the ability to learn and improve from experience without being explicitly programmed.
Mel Frequency Cepstral Coefficients (Mfcc)
Mel Frequency Cepstral Coefficients (MFCC) are a feature extraction technique widely used in speech recognition to represent the spectral characteristics of speech.
Mel Scale
Mel Scale is a perceptual scale of pitches that approximates the human ear's response to different frequencies.
Mel Spectrogram
A Mel Spectrogram is a spectrogram representation of an audio signal in which the frequency scale is transformed to better correspond with human perception of sound.
Mel-Frequency Cepstral Coefficients (Mfccs)
Mel-Frequency Cepstral Coefficients (MFCCs) are widely used features for speech recognition, representing the power spectrum of a speech signal.
Meta-Learning
Meta-learning, also known as learning to learn, is a field of machine learning that focuses on algorithms and techniques for automatic machine learning algorithm design.
Multilingual Speech Recognition
Multilingual Speech Recognition is the capability of a speech recognition system to recognize and transcribe speech in multiple languages.
Multimodal Speech Recognition
Multimodal Speech Recognition is the task of combining information from multiple modalities, such as audio and visual data, to improve speech recognition performance.
N
Neural Language Model
Neural Language Model is a type of language model that is based on neural network architectures.
Neural Network
A Neural Network refers to a computational model inspired by the structure and function of the human brain, consisting of interconnected artificial neurons.
Neural Network Architecture
Neural Network Architecture refers to the design and structure of a neural network, including the number and arrangement of layers and nodes.
Neural Turing Machine (Ntm)
Neural Turing Machine (NTM) is a neural network architecture that combines an external memory with a controller to enable more complex computations.
Noise Adaptation
Noise Adaptation is the process of adapting a speech recognition system to perform well in the presence of specific types of noise or acoustic conditions.
Noise Cancellation
Noise Cancellation is the process of reducing or eliminating unwanted background noise in an audio signal to enhance speech intelligibility.
Noise Reduction
Noise Reduction is the process of removing unwanted noise from a speech signal to enhance its quality.
Noise Robustness
Noise Robustness refers to the ability of a speech recognition system to perform accurately even in the presence of background noise or adverse acoustic conditions.
Noise Suppression
Noise Suppression is the process of reducing background noise in an audio signal to improve the intelligibility and quality of speech.
O
Overfitting
Overfitting occurs when a machine learning model is overly optimized for the training data and performs poorly on unseen or new data.
P
Perplexity
Perplexity is a metric used to evaluate the performance of a language model by measuring how well it predicts a sequence of words.
Phone Recognition
Phone Recognition is the task of recognizing the phonemes or speech sounds in a given speech signal.
Phoneme
A Phoneme is the smallest unit of sound that distinguishes one word from another in a particular language.
Phonetic Segmentation
Phonetic Segmentation is the process of dividing a speech signal into phonetic units, such as phonemes or syllables.
Pitch
Pitch refers to the perceived frequency or tone of a sound, determining whether it is high or low.
Pitch Detection
Pitch Detection is the process of estimating the fundamental frequency of a speech signal, which corresponds to the perceived pitch.
R
Recurrent Neural Network (Rnn)
A Recurrent Neural Network (RNN) is a type of artificial neural network designed to process sequential data by maintaining information about past inputs.
Robustness
Robustness in speech recognition refers to the ability of a system to maintain high accuracy under various challenging conditions, such as noise, accent, or background interference.
S
Segmentation
Segmentation is the process of dividing a continuous speech signal into smaller segments to facilitate further processing.
Semi-Supervised Learning
Semi-Supervised Learning is a machine learning approach that combines labeled and unlabeled data to train a model, leveraging the benefits of both types of data.
Speaker Adaptation
Speaker Adaptation is the process of customizing a speech recognition system to recognize an individual speaker's acoustic characteristics and speech patterns.
Speaker Diarization
Speaker Diarization is the process of segmenting and identifying individual speakers in an audio recording.
Speaker Verification
Speaker Verification is the task of authenticating or verifying the claimed identity of a speaker by comparing their voice characteristics with stored voiceprints.
Spectrogram
A Spectrogram is a visual representation of the frequency content of an audio signal over time.
Speech Recognition
Speech Recognition is the ability of a machine or program to identify and understand spoken language, converting it into written text or interpreting its meaning.
Sphinx
Sphinx is a popular open-source speech recognition toolkit created by Carnegie Mellon University, offering tools and libraries for developing speech recognition systems.
Statistical Language Model (Slm)
A Statistical Language Model (SLM) is a language model based on statistical properties of texts, used to estimate the likelihood of word sequences.
Streaming Speech Recognition
Streaming Speech Recognition is the task of performing real-time speech recognition on streaming audio data.
Subword Units
Subword Units are linguistic units that are smaller than words, such as subword or character n-grams.
Supervised Learning
Supervised Learning is a machine learning approach in which a model learns from labeled data, making predictions or classifications based on input-output pairs.
T
Transfer Learning
Transfer Learning is a machine learning technique in which knowledge gained from one task is applied to another related task, often improving performance and reducing training data requirements.
Triphone
A Triphone is a context-dependent speech unit consisting of three phonemes, taking into account the influence of surrounding phonetic context.
U
Underfitting
Underfitting occurs when a machine learning model is too simple or not trained enough, resulting in poor performance on both training and unseen data.
Unsupervised Learning
Unsupervised Learning is a machine learning approach in which a model learns patterns or structures in unlabeled data, without explicit input-output pairs.
V
Voice Activity Detection (Vad)
Voice Activity Detection (VAD) is the task of detecting the presence or absence of human speech in an audio signal.
Voice Command Recognition
Voice Command Recognition is the task of recognizing spoken commands or instructions, typically used in voice-controlled systems.
W
Wake Word
A Wake Word is a specific trigger word or phrase that activates a voice-controlled system or virtual assistant, such as 'Hey Siri' or 'Alexa'.
Word Boundary Detection
Word Boundary Detection is the task of determining the boundaries between words in a speech signal.
Word Embeddings
Word Embeddings are dense vector representations of words, typically learned from large amounts of text data using techniques such as Word2Vec or GloVe.
Word Error Rate (Wer)
Word Error Rate (WER) is a metric used to measure the accuracy of a speech recognition system by comparing the number of word errors in the recognized output to the reference transcription.
Word Spotting
Word Spotting is a speech recognition task that involves finding occurrences of specific words within a large collection of spoken utterances.
Word-Level Alignment
Word-Level Alignment is the task of aligning words in a recognized transcription with their corresponding words in the reference transcription, often used in evaluating the performance of a speech recognition system.
Z
Zero Padding
Zero Padding is a technique used to increase the length of sequential data by adding zeros at the beginning or end of the sequence.
Zero-Crossing Rate
Zero-Crossing Rate is a feature used in speech and audio processing to estimate the rate at which a signal changes its sign.