AI concepts explained#

Acoustic Model#

An acoustic model is a machine learning model that is used in automatic speech recognition systems to map spoken audio to transcribed text. It is a key component of a speech recognition system, and it is responsible for converting the raw audio signal of spoken language into a form that can be understood and processed by the system.

The acoustic model is typically trained on a large dataset of transcribed audio data, which is used to learn the statistical patterns and features that are indicative of different words and sounds in the language. The model is then used to transcribe new audio data by matching the patterns and features it has learned to the input audio signal.

The acoustic model is usually just one part of a larger speech recognition system, which may also include components such as a language model and a pronunciation model. Together, these components work to accurately transcribe spoken language into text.

Algorithm#

An algorithm is a set of steps or instructions that are followed in order to solve a problem or achieve a goal. In the context of computing, algorithms are used to process data and perform tasks.

Algorithms can be simple or complex, and they can be implemented using a variety of programming languages and technologies. Some common characteristics of algorithms include:

Input: An algorithm takes in input data, which it processes to produce an output.
Output: An algorithm produces an output based on the input data it receives.
Definiteness: An algorithm must be precise and unambiguous, with clear instructions for each step.
Finiteness: An algorithm must have a finite number of steps and must eventually terminate.
Effectiveness: An algorithm must be able to be implemented in a way that it can be carried out in a finite amount of time.

Algorithms are used in a wide range of fields, including computer science, mathematics, and data analysis. They are an essential tool for automating tasks and solving problems in a systematic and efficient way.

Deep Learning#

Deep learning is a subfield of machine learning that is inspired by the structure and function of the human brain, specifically the neural networks that make up the brain. It involves the use of artificial neural networks, which are computational models that are designed to process data in a way that is similar to the way the brain processes information.

Deep learning algorithms are able to learn and adapt to new information through the use of multiple layers of interconnected nodes, which process and transform the input data. These layers of nodes can be “deep,” hence the name “deep learning,” and they allow the algorithm to learn and understand complex patterns and relationships in the data.

Deep learning has been successful in a wide range of applications, including image and speech recognition, natural language processing, and machine translation. It has also been used to power some of the most impressive achievements in AI, such as beating human champions at complex games like Go.

Domain Adaptation#: Domain Adaptation is the process of fine-tuning a language model in order to increase its output quality in a specific domain. In the case of ASR, for example, this process can lead to the correct recognition of domain specific terminology.

Language Model#

A language model is a statistical model that is used to predict the likelihood of a sequence of words. In natural language processing (NLP), language models are used to predict the likelihood of a sequence of words in a particular language. The basic idea is that given a sequence of words, a language model assigns a probability to each possible word that might come next in the sequence.

For example, consider the following sentence: “The cat sat on the ________.” A language model might predict that the word “mat” is the most likely word to come next in the sequence, followed by “couch” and “chair.”

Language models are typically trained on large amounts of text data and use techniques from machine learning to learn the patterns and structure of the language. They can be used for a variety of NLP tasks, including language generation, machine translation, and text classification.

Language models can be used e.g. to help improve texts and correct the acoustic models predictions in ASR.

Sentiment Analysis#

Sentiment analysis is a subfield of natural language processing (NLP) that involves using computational techniques to identify and extract subjective information from text data. It is often used to determine the overall sentiment or emotion of a piece of writing, such as whether it is positive, negative, or neutral.

In the context of AI, sentiment analysis involves training machine learning models on large amounts of annotated text data to recognize patterns and features that are indicative of different sentiments. The models can then be used to automatically classify new text data as having a particular sentiment.

Sentiment analysis is commonly used in a variety of applications, including social media monitoring, brand reputation management, and customer service. It can be useful for understanding how people feel about a particular topic or product, and can help businesses and organizations to make data-driven decisions about how to engage with their audience.

Simultaneous Interpretation#: Simultaneous interpretation is a form of interpretation in which the interpreter listens to the source language and simultaneously conveys the meaning of the source language to the target language. Simultaneous interpretation is often used in situations where it is not practical for the speaker to pause for interpretation, such as at conferences, meetings, and other events where multiple languages are spoken. Simultaneous interpreters typically work in pairs, taking turns interpreting the source language so that they can rest and avoid fatigue. They may also use specialized equipment, such as headsets and microphones, to facilitate the interpretation process.

Sparsity problem#: In a data set with many rare features and combinations of features, in most instances of the data set only a few of these are observed. Imagine for example a twitter corpus where all occurring words and bigrams are counted. Most of the word counts for a single tweet will be zero. This can lead to a variety of problems, starting from a waste of data storage.

Speaker diarization#: In an audio file with more than one person speaking, it assigns the transcription to the person who has uttered those words.

Word Error Rate#: The industry standard measurement of how accurate an ASR transcription is. It measures the quantity of errors compared to a reference transcription considered to be the gold standard.