Hidden Markov model training with contaminated speech material for distant-talking speech recognition

✍ Scribed by Matassoni, Marco (author);Omologo, Maurizio (author);Giuliani, Diego (author);Svaizer, Piergiorgio (author)

• Publisher: Academic Press
• Year: 2002
• Tongue: English
• Weight: 280 KB
• Volume: 16
• Category: Article
• ISSN: 0885-2308
• DOI: 10.1006/csla.2002.0191

✦ Synopsis

A challenging scenario is addressed in which a distant-talking speech recognizer operates in a noisy office environment with model adaptation. The use of a single far microphone as well as that of a microphone array input are investigated.

In addition to the benefits from the application of microphone array processing, system robustness is improved by training hidden Markov models (HMMs) with a contaminated version of a clean corpus. This artificial corpus is produced by exploiting information extracted from "real world" acoustic scenarios. The resulting models are then used as a starting point for unsupervised incremental adaptation.

Experimental results show that improvements in recognition accuracy due to multiple microphones, HMM training on contaminated speech and incremental adaptation are additive on a connected digits task. Moreover, the results show that unsupervised incremental adaptation receives the benefits of starting from models trained using contaminated speech. A final contribution of the paper refers to the influence of accuracy of speech activity detection, which seems to be relevant when moving towards real applications.