Semi-Supervised Model Training for Unbounded Conversational Speech Recognition

Understand

For conversational large-vocabulary continuous speech recognition (LVCSR) tasks, up to about two thousand hours of audio is commonly used to train state of the art models.

• Collection of labeled conversational audio however, is prohibitively expensive, laborious and error-prone.
• Furthermore, academic corpora like Fisher English (2004) or Switchboard (1992) are inadequate to train models with sufficient accuracy in the unbounded space of conversational speech.
• These corpora are also timeworn due to dated acoustic telephony features and the rapid advancement of colloquial vocabulary and idiomatic speech over the last decades.