Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews
This work addresses robust speech recognition for specific domains like oral history, but it is incremental as it combines existing techniques.
The paper tackled the problem of limited training data for robust speech recognition in challenging acoustic conditions, achieving a 19.3% relative reduction in word error rate on German oral history interviews.
In automatic speech recognition, often little training data is available for specific challenging tasks, but training of state-of-the-art automatic speech recognition systems requires large amounts of annotated speech. To address this issue, we propose a two-staged approach to acoustic modeling that combines noise and reverberation data augmentation with transfer learning to robustly address challenges such as difficult acoustic recording conditions, spontaneous speech, and speech of elderly people. We evaluate our approach using the example of German oral history interviews, where a relative average reduction of the word error rate by 19.3% is achieved.