Paper Info
Title
Target-Aware Language Models For Spoken Language Recognition
Abstract
This paper studies a new way of constructing multiple phone tokenizers for language recognition. In this approach, each phone tokenizer for a target language will share a common set of acoustic models, while each tokenizer will have a unique phone-based language model (LM) trained for a specific target language. The target-aware language models (TALM) are constructed to capture the discriminative ability of individual phones for the desired target languages. The parallel phone tokenizers thus formed are shown to achieve better performance than the original phone recognizer. The proposed TALM is very different from the LM in the traditional PPRLM technique. First of all, the TALM applies the LM information in the front-end as opposed to PPRLM approach which uses a LM in the system back-end; Furthermore, the TALM exploits the discriminative phones occurrence statistics, which are different from the traditional n-gram statistics in PPRLM approach. A novel way of training TALM is also studied in this paper. Our experimental results show that the proposed method consistently improves the language recognition performance on NIST 1996, 2003 and 2007 LRE 30-second closed test sets.
Year
Venue
Keywords
2009
INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5
spoken language recognition, parallel phone tokenizer, target-oriented phone tokenizer, target-aware language model, universal phone recognizer
Field
DocType
Citations 
Language transfer,Computer science,Comprehension approach,Object language,Universal Networking Language,Language identification,Artificial intelligence,Natural language processing,Picture language,Spoken language,Language technology
Conference
3
PageRank 
References 
Authors
0.42
1
5
Name
Order
Citations
PageRank
Rong Tong110811.33
Bin Ma260047.26
Haizhou Li33678334.61
Eng Siong Chng4970106.33
Kong-Aik Lee570960.64