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This paper presents the application of a multi-scale paradigm to Chinese spoken document retrieval (SDR) for improving retrieval performance. Multi-scale refers to the use of both words and subwords for retrieval. Words are basic units in a language that carry lexical meaning and subword units (such as phonemes, syllables or characters) are building components for words. Retrieval using subword indexing units is found to perform better than words because of the robustness of subword units to out-of-vocabulary (OOV) words during speech recognition and ambiguities in word segmentation. Experimental results have demonstrated that subword bigrams can bring improvement in retrieval performance over words (~9.56%). Application of multi-scale fusion to SDR aims at combining the lexical information of words and the robustness of subwords. This work presents the first detailed investigation for a Cantonese broadcast news retrieval task using two different multi-scale fusion approaches: pre-retrieval fusion and post-retrieval fusion.Multi-scale retrieval using both words and syllable bigrams achieve improvement in retrieval performance (~1.90%) over retrieval on the composite scales.

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Grapheme-to-phoneme conversion based on high-order Markov chain for spoken term detection by text query

Prozorov

Tatarinova

2017

2017 IEEE East-West Design &Amp; Test Symposium (EWDTS)

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Cited by 8 publications

References 16 publications

A novel approach to perform context‐based automatic spoken document retrieval of political speeches based on wavelet tree indexing

A novel approach to perform context‐based automatic spoken document retrieval of political speeches based on wavelet tree indexing

Multi-Scale Spoken Document Retrieval for Cantonese Broadcast News

Grapheme-to-phoneme conversion based on high-order Markov chain for spoken term detection by text query

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