| Speech Classification |
| ================================== |
| Speech Classification refers to a set of tasks or problems of getting a program to automatically classify input utterance or audio segment into categories, |
| such as Speech Command Recognition (multi-class), Voice Activity Detection (binary or multi-class), and Audio Sentiment Classification (typically multi-class), etc. |
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| **Speech Command Recognition** is the task of classifying an input audio pattern into a discrete set of classes. |
| It is a subset of Automatic Speech Recognition (ASR), sometimes referred to as Key Word Spotting, in which a model is constantly analyzing speech patterns to detect certain "command" classes. |
| Upon detection of these commands, a specific action can be taken by the system. |
| It is often the objective of command recognition models to be small and efficient so that they can be deployed onto low-power sensors and remain active for long durations of time. |
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| **Voice Activity Detection (VAD)** also known as speech activity detection or speech detection, is the task of predicting which parts of input audio contain speech versus background noise. |
| It is an essential first step for a variety of speech-based applications including Automatic Speech Recognition. |
| It serves to determine which samples to be sent to the model and when to close the microphone. |
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| **Spoken Language Identification (Lang ID)** also known as spoken language recognition, is the task of recognizing the language of the spoken utterance automatically. |
| It typically serves as the prepossessing of ASR, determining which ASR model would be activate based on the language. |
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| The full documentation tree is as follows: |
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| .. toctree:: |
| :maxdepth: 8 |
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| models |
| datasets |
| results |
| configs |
| resources.rst |
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| .. include:: resources.rst |
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