Discounted likelihood linear regression for rapid adaptation
نویسندگان
چکیده
Rapid adaptation schemes that employ the EM algorithm may suffer from overtraining problems when used with small amounts of adaptation data. An algorithm to alleviate this problem is derived within the information geometric framework of Csiszár and Tusnády, and is used to improve MLLR adaptation on NAB and Switchboard adaptation tasks. It is shown how this algorithm approximately optimizes a discounted likelihood criterion.
منابع مشابه
Discounted likelihood linear regression for rapid speaker adaptation
The widely used maximum likelihood linear regression speaker adaptation procedure suffers from overtraining when used for rapid adaptation tasks in which the amount of adaptation data is severely limited. This is a well known difficulty associated with the expectation maximization algorithm. We use an information geometric analysis of the expectation maximization algorithm as an alternating min...
متن کاملConvergence of DLLR Rapid Speaker Adaptation Algorithms
Discounted Likelihood Linear Regression (DLLR) is a speaker adaptation technique for cases where there is insufficient data for MLLR adaptation. Here, we provide an alternative derivation of DLLR by using a censored EM formulation which postulates additional adaptation data which is hidden. This derivation shows that DLLR, if allowed to converge, provides maximum likelihood solutions. Thus the ...
متن کاملImprovement of MLLR Speaker Adaptation Using a Novel Method
This paper presents a technical speaker adaptation method called WMLLR, which is based on maximum likelihood linear regression (MLLR). In MLLR, a linear regression-based transform which adapted the HMM mean vectors was calculated to maximize the likelihood of adaptation data. In this paper, the prior knowledge of the initial model is adequately incorporated into the adaptation. A series of spea...
متن کاملMaximum Likelihood Linear Regression (MLLR) for ASR Severity Based Adaptation to Help Dysarthric Speakers
Automatic speech recognition (ASR) for dysarthric speakers is one of the most challenging research areas. The lack of corpus for dysarthric speakers makes it even more difficult. The speaker adaptation (SA) is an alternative solution to overcome the lack of dysarthric speech and enhance the performance of ASR. This paper introduces the Severity-based adaptation, using small amount of speech dat...
متن کاملUsing maximum likelihood linear regression for segment clustering and speaker identification
Many adaptation scenarios rely on clustering of either the test or training data. Although consistency between the clustering and adaptation objective functions is desired, most previous approaches have not implemented such consistency. This paper shows that the statistics used in Maximum Likelihood Linear Regression (MLLR) adaptation are su cient to cluster data with a consistent Maximum Likel...
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 1999