Abstract book (pdf) - ICPR 2010

16:00-16:20, Paper TuCT6.2
Modeling Syllable-Based Pronunciation Variation for Accented Mandarin Speech Recognition
Zhang, Shilei, IBM Res.
Shi, Qin, IBM Res. – China
Qin, Yong, IBM Res. – China
Pronunciation variation is a natural and inevitable phenomenon in an accented Mandarin speech recognition application.
In this paper, we integrate knowledge-based and data-driven approaches together for syllable-based pronunciation variation
modeling to improve the performance of Mandarin speech recognition system for speakers with Southern accent. First,
we generate the syllable-based pronunciation variation rules of Southern accent observed from the training corpus by Chinese
linguistic expert. Second, dictionary augmentation with multiple pronunciation variants and pronunciation probability
derived from forced alignment statistics of training data. The acoustic models will be retrained based on the new expansion
dictionary. Finally, pronunciation variation adaptation will be performed to further fit the data on the decoding stage by
taking distribution of variation rules clusters of testing set into account. The experimental results show that the proposed
method provides a flexible framework to improve the recognition performance for accented speech effectively.
16:20-16:40, Paper TuCT6.3
Automatic Pronunciation Transliteration for Chinese-English Mixed Language Keyword Spotting
Zhang, Shilei, IBM Res.
Shuang, Zhiwei, IBM Res. – China
Qin, Yong, IBM Res. – China
This paper presents automatic pronunciation transliteration method with acoustic and contextual analysis for Chinese-
English mixed language keyword spotting (KWS) system. More often, we need to develop robust Chinese-English mixed
language spoken language technology without Chinese accented English acoustic data. In this paper, we exploit pronunciation
conversion method based on syllable-based characteristic analysis of pronunciation and data-driven phoneme pairs
mappings to solve mixed language problem by only using well-trained Chinese models. One obvious advantage of such
method is that it provides a flexible framework to implement the pronunciation conversion of English keywords to Chinese
automatically. The efficiency of the proposed method was demonstrated under KWS task on mixed language database.
16:40-17:00, Paper TuCT6.4
Learning Virtual HD Model for Bi-Model Emotional Speaker Recognition
Huang, Ting, Zhejiang Univ.
Yang, Yingchun, Zhejiang Univ.
Pitch mismatch between training and testing is one of the important factors causing the performance degradation of the
speaker recognition system. In this paper, we adopted the missing feature theory and specified the Unreliable Region (UR)
as the parts of the utterance with high emotion induced pitch variation. To model these regions, a virtual HD (High Different
from neutral, with large pitch offset) model for each target speaker was built from the virtual speech, which were converted
from the neutral speech by the Pitch Transformation Algorithm (PTA). In the PTA, a polynomial transformation function
was learned to model the relationship of the average pitch between the neutral and the high-pitched utterances. Compared
with traditional GMM-UBM and our previous method, our new method obtained 1.88% and 0.84% identification rate
(IR) increase on the MASC respectively, which are promising results.
17:00-17:20, Paper TuCT6.5
Role of Synthetically Generated Samples on Speech Recognition in a Resource-Scarce Language
Chakraborty, Rupayan, St. Thomas’ Coll. of Eng. & Tech.
Garain, Utpal, Indian Statistical Inst.
Speech recognition systems that make use of statistical classifiers require a large number of training samples. However,
collection of real samples has always been a difficult problem due to the involvement of substantial amount of human intervention
and cost. Considering this problem, this paper presents a novel method for generating synthetic samples from
a handful of real samples and investigates the role of these samples in designing a speech recognition system. Speaker dependent
limited vocabulary isolated word recognition in an Indian language (i.e. Bengali) has been taken a reference to
demonstrate the potential of the proposed framework. The role of synthetic samples is demonstrated by showing a significant
improvement in recognition accuracy. A maximum improvement of 10% is achieved using the proposed approach.
- 127 -