| Paper: | SP-L11.1 | ||
| Session: | Language Modeling and Search | ||
| Time: | Friday, May 21, 15:30 - 15:50 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Language Modeling | ||
| Title: | META-DATA CONDITIONAL LANGUAGE MODELING | ||
| Authors: | Michiel Bacchiani; AT&T Labs - Research | ||
| Brian Roark; AT&T Labs - Research | |||
| Abstract: | Automatic Speech Recognition (ASR) often occurs in circumstances in which knowledge external to the speech signal, or meta-data, is given. For example, a company receiving a call from a customer might have access to a database record of that customer. Conditioning the ASR models directly on this information to improve the transcription accuracy is hampered because, generally, the meta-data takes on many values and a training corpus will have little data for each meta-data condition. This paper presents an algorithm to construct language models conditioned on such meta-data. It uses tree-based clustering of the the training data to automatically derive meta-data projections, useful as language model conditioning contexts. The algorithm was tested on a multiple domain voicemail transcription task. We compare the performance of an adapted system aware of the domain shift to a system that only has meta-data to infer that fact. The meta-data used were the callerID strings associated with the voicemail messages. The meta-data adapted system matched the performance of the system adapted using the domain knowledge explicitly. | ||
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