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**Speech recognition algorithms** use a type of machine learning called deep learning, which involves training artificial neural networks on large datasets to improve accuracy.
**Audio signals are converted to spectrograms**, which are visual representations of sound waves, to help machines understand speech patterns.
**Transcription software uses Natural Language Processing (NLP)** to analyze and contextualize spoken words, ensuring accurate translation into written text.
**Frequency analysis** is used to distinguish between similar-sounding words, such as "to", "too", and "two", by identifying unique sound wave patterns.
**Acoustic models** are used to recognize spoken words based on the physical properties of sound waves, including pitch, tone, and vowel sounds.
**Language models** predict the likelihood of a word or phrase occurring in a sentence based on context, grammar, and syntax.
**ASR (Automatic Speech Recognition) systems** can be trained to recognize specific accents, dialects, and speaking styles to improve transcription accuracy.
**Mel-Frequency Cepstral Coefficients (MFCCs)** are used to extract features from audio signals, allowing machines to distinguish between similar sounds.
**Silence detection** algorithms are used to identify pauses in speech, allowing for more accurate transcription of spoken words.
**The Levenshtein distance algorithm** measures the minimum number of operations (insertions, deletions, and substitutions) needed to transform one string into another, helping to identify errors in transcription.
**Decision trees** are used to classify spoken words into different categories, such as nouns, verbs, and adjectives, to improve transcription accuracy.
**Markov chains** are used to model the probability of word sequences, allowing machines to predict the next word in a sentence.
**Hidden Markov models** are used to analyze the probability of observing a sequence of words, helping to improve transcription accuracy.
**Beam search algorithms** are used to efficiently search through possible transcriptions to find the most likely match.
**Active learning** involves identifying the most uncertain or ambiguous transcriptions and requesting human input to improve the accuracy of the transcription model.
Experience error-free AI audio transcription that's faster and cheaper than human transcription and includes speaker recognition by default! (Get started for free)