## Enhancements to Transformation-Based Speaker Adaptation: Principal Component and Inter-Class Maximum Likelihood Linear Regression (2000)

Citations: | 5 - 1 self |

### BibTeX

@TECHREPORT{Doh00enhancementsto,

author = {Sam-joo Doh},

title = {Enhancements to Transformation-Based Speaker Adaptation: Principal Component and Inter-Class Maximum Likelihood Linear Regression},

institution = {},

year = {2000}

}

### OpenURL

### Abstract

iii Abstract In this thesis we improve speech recognition accuracy by obtaining better estimation of linear transformation functions with a small amount of adaptation data in speaker adaptation. The major contributions of this thesis are the developments of two new adaptation algorithms to improve maximum likelihood linear regression. The first one is called principal component MLLR (PC-MLLR), and it reduces the variance of the estimate of the MLLR matrix using principal component analysis. The second one is called inter-class MLLR, and it utilizes relationships among different transformation functions to achieve more reliable estimates of MLLR parameters across multiple classes. The main idea of PC-MLLR is that if we estimate the MLLR matrix in the eigendomain, the variances of the components of the estimates are inversely proportional to their eigenvalues. Therefore we can select more reliable components to reduce the variances of the resulting estimates and to improve speech recognition accuracy. PC-MLLR eliminates highly variable components and chooses the principal components corresponding to the largest eigenvalues. If all the component are used, PC-MLLR becomes the same as conventional MLLR. Choosing fewer principal components increases the bias of the estimates which can reduce recognition accuracy. To compensate for this problem, we developed weighted principal component MLLR (WPC-MLLR). Instead of eliminating some of the components, all the components in WPC-MLLR are used after applying weights that minimize the mean square error. The component corresponding to a larger eigenvalue has a larger weight than the component corresponding to a smaller eigenvalue. As more adaptation data become available, the benefits from these methods may become smaller because ...

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Citation Context ...e assumed, and estimated using the MAP framework. Other research groups have proposed hybrid algorithms to combine the transformation-based and Bayesian adaptation approaches [8, 11]. Shinoda and Lee =-=[52]-=- proposed a structured MAP (SMAP) adaptation, in which the transformation parameters are estimated in a hierarchical structure. Despite its name, the SMAP algorithm does not make use of prior informat... |

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Citation Context ...rocedure we will need to estimate or predict the characteristics of the test speaker. Similar topics have been studied in Bayesian framework where the prior distributions of model parameters are used =-=[28]-=-. 6.2.2 Control of Weights in Inter-Class MLLR Using neighboring classes to estimate the parameters in the target class has both advantages and disadvantages. Neighboring classes are helpful because t... |

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Citation Context ...entences each. The Spoke 0 is similar to the Spoke 3 except that they are from native speakers. • The Telefónica (TID) corpus This corpus was recorded over the GSM cellular telephone network in Spa=-=in [25]-=-. It is a small vocabulary set (approximately 75 words in lexicon) which contain mainly numbers. It includes approximately 6000 training utterances and 3000 testing utterances (with approximately 1450... |

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Citation Context ...d MAP (EMAP) adaptation which makes use of information about correlations among parameters. A parameter can be adapted even though there is no adaptation data directly associated with it. Zavaliagkos =-=[56]-=- was the first to apply EMAP adaptation to large scale HMM speech recognizers. While the EMAP approach produces an elegant analytical adaptation equation that makes appropriate use of correlations amo... |

8 |
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Citation Context ...of the EMAP algorithm at the expense of a finite misadjustment. Huo and Lee [27] proposed an algorithm based on pairwise correlation of parameter pairs to reduce computational complexity. Afify et al =-=[3]-=- also used pairwise correlation of parameters. For a target parameter, they got several estimates using correlations with different parameters, and combine them to get the final estimate for the targe... |

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Citation Context ...n of parameters. For a target parameter, they got several estimates using correlations with different parameters, and combine them to get the final estimate for the target parameter. Chen and DeSouza =-=[6]-=- also used the correlation between speech units for a similar speaker adaptation algorithm which they refer to as Adaptation By Correlation (ABC). The estimates are derived using least squares theory,... |

8 |
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8 |
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Citation Context ...data. The original data which consisted of interrelated variables are transformed into a new set of uncorrelated variables by the eigenvectors of the covariance matrix of the original data set. Nouza =-=[44] use-=-d PCA for feature selection in a speech recognition system. Kuhn et al. [34] introduced “eigenvoices” to represent the prior knowledge of speaker variation. Hu [23] applied PCA to describe the cor... |

6 | Inter-Class MLLR for Speaker Adaptation
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(Show Context)
Citation Context ...quare Error 0.2 0.15 0.1 0.05 5.3 Inter-Class MLLR We apply the method described in the previous section to the MLLR framework, and develop a new adaptation procedure which is called inter-class MLLR =-=[13]-=-. In inter-class MLLR the inter-class transformation is given by another linear regression. The inter-class transformation provides a priori knowledge among the regression classes, and helps to estima... |

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6 |
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Citation Context ...d into a new set of uncorrelated variables by the eigenvectors of the covariance matrix of the original data set. Nouza [44] used PCA for feature selection in a speech recognition system. Kuhn et al. =-=[34] int-=-roduced “eigenvoices” to represent the prior knowledge of speaker variation. Hu [23] applied PCA to describe the correlation between phoneme classes for speaker normalization. While the general mo... |

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5 |
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Citation Context ...of the original data set. Nouza [44] used PCA for feature selection in a speech recognition system. Kuhn et al. [34] introduced “eigenvoices” to represent the prior knowledge of speaker variation.=-= Hu [23]-=- applied PCA to describe the correlation between phoneme classes for speaker normalization. While the general motivation for these approaches was similar to the approach described in this thesis, none... |

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4 |
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Citation Context ...imized. He also described (5.1) (5.2) (5.3)sChapter 5. Inter-Class MLLR 57 a method in which MLLR was applied to the root node and the sub-nodes of the regression class tree iteratively. Chien et al. =-=[9]-=- described a hierarchical adaptation procedure that used a tree structure to control the transformation sharing and to obtain reliable transformation parameters. As we have noted above, new parameters... |

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Citation Context ... an elegant analytical adaptation equation that makes appropriate use of correlations among adaptation parameters, solution of the equation depends on the inversion of a large matrix. Rozzi and Stern =-=[49]-=- developed a least mean square (LMS) algorithm for an efficient computation of the EMAP algorithm at the expense of a finite misadjustment. Huo and Lee [27] proposed an algorithm based on pairwise cor... |

3 | Weighted principal component MLLR for speaker adaptation
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(Show Context)
Citation Context ...e larger. We determine the best p value in advance using development data which are similar to the actual test data. 4.3 Principal Component MLLR The formulation of Principal Component MLLR (PC-MLLR) =-=[12] is -=-very similar to that discussed in the previous section, except that we also consider the a posteriori probability γ t( k) as well as the baseline 2 variance σk and the shift vector b . Let’s consi... |

2 | Acoustic-Feature-Based Frequency Warping for Speaker Normalization
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Citation Context ...odel parameters as shown in Fig. 2.4 [36]. Cepstral mean normalization (CMN) (or cepstral mean subtraction [48]), codedependent cepstral normalization (CDCN) [1], and vocal tract length normalization =-=[20]-=- are some of the examples of the method which modifies input feature vectors. Sankar and Lee [51] proposed a maximumlikelihood stochastic matching approach to estimate an inverse distortion function. ... |

2 |
et al.: 1994 Benchmark Tests for the ARPA Spoken Language Program
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Citation Context ...especially on the non-native English speakers from Spoke 3 in the 1994 DARPA evaluation (s3-94). Spoke 3 is designed “to evaluate a rapid enrollment speaker adaptation algorithm on difficult speaker=-=s [45].�-=-�� It consists of 10 speakers reading 20 test sentences each. Each speaker reads news articles from the WSJ corpus. The primary test in Spoke 3 uses 40 adaptation sentences for each speaker in supervi... |

2 |
et al, “1998 Broadcast news benchmark test results: English and non-English word error rate performance measures
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(Show Context)
Citation Context ...onditions. It contains a broad sample of speaker and environmental conditions. • Broadcast News from the 1998 DARPA Evaluation (Hub 4) The Hub 4 corpus contains recorded speech from TV broadcast new=-=s [46]-=-. It is a mixture of several different conditions, e.g. planned/spontaneous, clean/noisy, full/narrow bandwidth, and native/nonnative. It has a much larger vocabulary than the previous corpora. The ba... |