Spontaneous Mandarin speech understanding using utterance classification: a case study

As speech recognition matures and becomes more

practical in commercial English applications, localization has

quickly become the bottleneck for having more speech features.

Not only are some technologies highly language dependent, there

are simply not enough speech experts in the large number of

target languages to develop the data modules and investigate

potential performance related issues. This paper shows how data

driven methods like Utterance Classification (UC) successfully

address these major issues. Our experiments demonstrate that

UC performs as well as or better than hand crafted Context Free

Grammars (CFGs) for spontaneous Mandarin speech

understanding, even when applied without linguistic knowledge.

We also discuss two pragmatic modifications of the UC algorithm

adopted to handle multiple choice answers and to be more robust

to feature selections.

MandarinUtteranceClassification-CameraReady.pdf
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In  International Symposium on Chinese Spoken Language Processing

Publisher  International Speech Communication Association

Details

TypeInproceedings
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