How does data augmentation for speech recognition improve training speed?

How does data augmentation for speech recognition improve training speed?

Given a spectrogram, you can view it as an image where x axis is time while y axis is frequency. Intuitively, it improves training speed because no data transformation between waveform data to spectrogram data but augmenting spectrogram data.

How much data is needed for speech recognition?

To address issues like word deletion or substitution, a significant amount of data is required to improve recognition. Generally, it’s recommended to provide word-by-word transcriptions for 1 to 20 hours of audio. However, even as little as 30 minutes can help to improve recognition results.

How to prepare data for custom speech-speech service?

If possible, include at least a half-second of silence before and after speech in each sample file. While audio with low recording volume or disruptive background noise is not helpful, it should not hurt your custom model. Always consider upgrading your microphones and signal processing hardware before gathering audio samples.

Is it possible to create a speech recognition system?

Programming that does not include “human reason” and “human behavior” factors cannot lead to an ideal speech recognition system. In many cases, the users’ voice commands are not recognized, or they are misunderstood.

How to use LM to improve speech recognition?

Parameter is denoted: In later experiment, three standard learning rate schedules are defined: B (asic): (sr, snoise, si, sf ) = (0.5k, 10k, 20k, 80k) LM is applied to further boost up the model performance.

Which is the best way to perform data augmentation?

Traditional way to perform data augmentation is normally applied to waveform. Park et al. go for another approach which is manipulate spectrogram. Given a spectrogram, you can view it as an image where x axis is time while y axis is frequency.