In natural language processing (NLP) the problem of named entity (NE) recognition in speech is well known, yet remains a challenge where performance is dependent on automatic speech recognition (ASR) system error rates. NEs are often foreign or out-of-vocabulary (OOV) words, leaving conventional ASR systems unable to recognize them. In our research, we improve a CRF-based NE recognition system by incorporating two styles of prosodic features, hypothesized ToBI labels and unsupervised clusters of acoustic features. ToBI-based features improve NE recognition by 6% absolute (F1:0.39 v.s. F1: 0.45) on automatically recognized spontaneous speech from ACE'05.
Bibliographic reference. Katerenchuk, Denys / Rosenberg, Andrew (2014): "Improving named entity recognition with prosodic features", In INTERSPEECH-2014, 293-297.