Smoothing Factor in Discriminative Feature Adaptation
نویسنده
چکیده
In these days, Discriminative Training (DT) methods of an acoustics model are taking over the leadership in the speaker recognition task for training an acoustics model. Maximum Likelihood (ML) training suffers from some inaccuracies because of improper assumptions of the suitability of the HMM. Well-known adaptation method, feature Maximum Likelihood Linear Regression (fMLLR), is based on ML criterion:
منابع مشابه
Discriminative Adaptive Training Using the Mpe Criterion
This paper addresses the use of discriminative training criteria for Speaker Adaptive Training (SAT), where both the transform generation and model parameter estimation are estimated using the Minimum Phone Error (MPE) criterion. In a similar fashion to the use of I-smoothing for standard MPE training, a smoothing technique is introduced to avoid over-training when optimizing MPEbased feature-s...
متن کاملImprovements to generalized discriminative feature transformation for speech recognition
Generalized Discriminative Feature Transformation (GDFT) is a feature space discriminative training algorithm for automatic speech recognition (ASR). GDFT uses Lagrange relaxation to transform the constrained maximum likelihood linear regression (CMLLR) algorithm for feature space discriminative training. This paper presents recent improvements on GDFT, which are achieved by regularization to t...
متن کاملRegularized feature-space discriminative adaptation for robust ASR
Model-space adaptation techniques such as MLLR and MAP are often used for porting old acoustic models into new domains. Discriminative schemes for model adaptation based on MMI and MPE objective functions are also utilized. For feature-space adaptations, one extension to the wellknown feature-space discriminative training (fMPE) algorithm, feature-space discriminative adaptation, was recently p...
متن کاملدو روش تبدیل ویژگی مبتنی بر الگوریتم های ژنتیک برای کاهش خطای دسته بندی ماشین بردار پشتیبان
Discriminative methods are used for increasing pattern recognition and classification accuracy. These methods can be used as discriminant transformations applied to features or they can be used as discriminative learning algorithms for the classifiers. Usually, discriminative transformations criteria are different from the criteria of discriminant classifiers training or their error. In this ...
متن کاملGeneralized Discriminative Training for Speech Recognition
In speech recognition, discriminative training has proved to be an effective method to improve recognition accuracy. It has successfully improved systems of different scales and different languages. While discriminative training has been developing for over 20 years, it continues to draw attention to researchers and remains to be one of the most important topics in speech recognition to date. D...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010