Named Entity Recognition from Speech Using Discriminative Models and Speech Recognition Confidence
نویسندگان
چکیده
منابع مشابه
Discriminative named entity recognition of speech data using speech recognition confidence
This paper presents a method for the named entity recognition (NER) of speech data that uses automatic speech recognition (ASR) confidence as a feature that indicates whether each word is correctly recognized. An NER model is trained using ASR results with named entity (NE) labels to include an ASR confidence feature as well as corresponding transcriptions with NE labels. Experiments using supp...
متن کاملIncorporating Speech Recognition Confidence into Discriminative Named Entity Recognition of Speech Data
This paper proposes a named entity recognition (NER) method for speech recognition results that uses confidence on automatic speech recognition (ASR) as a feature. The ASR confidence feature indicates whether each word has been correctly recognized. The NER model is trained using ASR results with named entity (NE) labels as well as the corresponding transcriptions with NE labels. In experiments...
متن کاملOOV Sensitive Named-Entity Recognition in Speech
Named Entity Recognition (NER), an information extraction task, is typically applied to spoken documents by cascading a large vocabulary continuous speech recognizer (LVCSR) and a named entity tagger. Recognizing named entities in automatically decoded speech is difficult since LVCSR errors can confuse the tagger. This is especially true of out-of-vocabulary (OOV) words, which are often named e...
متن کاملDiscriminative models for speech recognition
The discriminative approach to speech recognition offers several advantages over the generative, such as a simple introduction of additional dependencies and direct modelling of sentence posterior probabilities/decision boundaries. However, the number of sentences that can possibly be encoded into an observation sequence can be vast, which makes the application of models, such as support vector...
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ژورنال
عنوان ژورنال: Journal of Information Processing
سال: 2009
ISSN: 1882-6652
DOI: 10.2197/ipsjjip.17.72