Speech

Speech data is a sequence of audio signals that represent spoken language.

It is used when there is a need to analyze or process spoken language data, such as voice commands, phone conversations, or podcasts. Speech data is commonly applied in scenarios such as speech recognition, speech synthesis, and voice control. Working with speech data involves converting the audio signals into a format that can be processed by machine learning models, often involving steps like feature extraction and signal processing.

Feature extraction is the process of converting raw audio signals into a set of features that can be used for analysis. For example, Mel-frequency cepstral coefficients (MFCCs) are commonly used features in speech recognition.

Signal processing involves techniques to enhance or manipulate the audio signals, such as noise reduction or normalization.

Speech data is important because it allows for the analysis and understanding of spoken language, enabling models to extract meaningful insights from audio information. It is a powerful approach in machine learning, enabling models to process and analyze human speech effectively.