What does a speech dataset consist of?

A speech dataset includes various components that are used to train and evaluate automatic speech recognition (ASR) systems.

1. Audio Files

2. Transcriptions or/and Annotation

3. Metadata

The primary component of a speech dataset is audio recordings of spoken language. These recordings can vary in length and quality and may include background noise or other environmental factors. Audio recordings are generally in .wav or .mp3 format.

Each audio file in a speech dataset is typically accompanied by a corresponding transcription, which is a written representation of the spoken words in the audio. Transcriptions are used to train ASR systems to recognize and transcribe speech accurately. The transcription file is generally in .json format.

Speech datasets may also include annotations that provide additional information about the audio recordings, such as the location of specific words or phrases, intent, outcome, sentiment of audio etc. Annotation elements are also represented as a .json file.

Metadata is a collection of information for each audio file including speaker information like gender, age, accent, other demographic information, background noise information, or other necessary information to help ASR model training. Metadata files can be in .xlxs or .json format.