- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
Speech-to-Text
What is Speech to Text?
Speech-to-text, also known as speech recognition, allows for the real-time transcription of audio streams into text. This is also known as computer speech recognition. Simply put, speech to text listens to verbal audio recordings and creates a written verbatim script. When users speak clearly, script accuracy rates exceed 95%. The transcribed text can be utilized by applications, tools, and devices as command input. There are two main types of speech to text: speaker dependent which is mostly used for dictation software and speaker independent which is used for phone applications.
How is Speech to Text Used?
Speech to text is used to help professionals in various fields in need of high quality transcriptions. Advances in technology have made speech to text transcription faster, cheaper, and more convenient than manual transcription. Speech to text is also important for equal access and digital accessibility.
Below are some real-life examples of speech-to-text:
1. Voice Typing
Apps allow users to dictate long texts. They can be used for texting, emails, and documents.
2. Voice Commanding
Users can trigger specific actions by voice. Examples of command and control are entering query text by voice and selecting menu items by voice.
3. Voice translation
Customers can use Speech-to-Text technology to communicate with users who speak different languages.
Speech to Text vs Transcription
Transcription is a human made version of speech to text. Instead of an application or algorithm listening to an audio and creating a verbatim script, a person will listen to the audio and type what is heard. Transcription is a much longer and more costly process than speech to text. Though speech to text still requires human input to run the system and ensure correctness of each script. With modern technological updates, speech to text most often outperforms transcription. Human transcription does offer the benefit of understanding accents, emotion and languages. Typically, human transcription performs best in terms of accuracy, while speech to text outperforms in speed and efficiency.
Advantages of Using Speech to Text
Speech to text allows users to improve several different daily processes, and prices vary based on the program used. It is cost efficient when compared to human transcription services. Some services are free but may not yield the highest level of quality. It can also offer a convenient and user-friendly alternative to typing, whether used for dictation, word processing, or navigating the web. Speech to text has allowed users with disabilities to type on and operate computers. As speech to text continues to develop it has been specialized to transcribe audio for industries with advanced technical language. These industries include the medical, construction, and technology fields.
How Does Speech to Text Work?
Speech to text software analyzes vibrations created from an individual when they speak. Vibrations and frequency are broken down and analyzed to create phonemes. Phonemes are units of sound that differentiate between different words. These phonemes are then run through mathematical equations to create sentences. These sentences reflect the original audio spoken by the user. This text can be consumed, displayed, and acted upon by applications, tools, and devices as command input. Different speech to text softwares produce results at varying speeds and accuracy levels.