NDLI: Development of a Chinese song name recognition system

Content Provider	IEEE Xplore Digital Library
Author	Shang Cai Zhen Zhang Ta Li Jielin Pan Yonghong Yan
Copyright Year	2011
Description	Author affiliation: Key Laboratory of Speech Acoustics and Content Understanding, Chinese Academy of Sciences, Beijing, China (Shang Cai; Zhen Zhang; Ta Li; Jielin Pan; Yonghong Yan)
Abstract	The development of automatic speech recognition (ASR) technology in recent years has made it possible for some intelligent query systems to use a voice interface. Automatic song selection is a practical and interesting application of ASR. In this paper we describe our efforts to build and improve a Chinese song name recognition system. It is a large vocabulary, speaker-independent system currently in commercial use. This is a typical example of large list recognition tasks. We use a new paradigm for lager list recognition. In this framework, the spoken query is recognized by a LVCSR module firstly and then the recognized result is used to search for the final song name. Unlike transcription tasks, such as Switchboard task, our LVCSR module is an in-domain application. We present some innovative optimizations that improve the song name recognition accuracy. The experimental result shows that there techniques make relative 7.36%, 26.87% and 32.71% error rate reduction for top 1, top 5 and top 10 results respectively upon the conventional grammar-constrained recognizer.
Starting Page	941
Ending Page	945
File Size	268946
Page Count	5
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022197
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training language model probability scale Accuracy Error analysis syllable based language model Hidden Markov models song name recognition Speech recognition Speech Acoustics large list recognition
Content Type	Text
Resource Type	Article

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