In recent work, we proposed the rational all-pass transform (RAPT) as the basis of a speaker adaptation scheme intended for use with a large vocabulary speech recognition system. It was shown that RAPT-based adaptation reduces to a linear transformation of cepstral means, much like the better known maximum likelihood linear regression (MLLR). In a set of speech recognition experiments conducted...

The accuracy of a speech recognition (SR) system depends on many factors, such as the presence of background noise, mismatches in microphone and language models, variations in speaker, accent and even speaking rates. In addition to fast speakers, even normal speakers will tend to speak faster when using a speech recognition system in order to get higher throughput. Unfortunately, state-of-the-a...

This paper investigates how to improve the acoustic modelling of non-native speech. For this purpose we present an adaptation technique to combine hidden Markov models of the source and the target language of a foreign language student. Such model combination requires a mapping of the mean vectors from target to source language. Therefore, three different mapping approaches, based on either pho...

In this paper, a novel speaker normalization method is presented and compared to a well known vocal tract length normalization method. With this method, acoustic observations of training and testing speakers are mapped into a normalized acoustic space through speaker-specific transformations with the aim of reducing inter-speaker acoustic variability. For each speaker, an affine transformation ...

This paper proposes model-space Maximum Likelihood Linear Regression (mMLLR) based speaker adaptation technique for trajectory HMMs, which have been derived from HMMs by imposing explicit relationships between static and dynamic features. This model can alleviate two limitations of the HMM: constant statistics within a state and conditional independence assumption of state output probabilities ...

Maximum likelihood linear regression (MLLR) is an adaptation technique suitable for both speaker and environmental model-based adaptation. The models are adapted using a set of linear transformations, estimated in a maximum likelihood fashion from the available adaptation data. As these transformations can capture general relationships between the original model set and the current speaker, or ...

Speaker adaptation is recognized as an essential part of today’s large-vocabulary automatic speech recognition systems. A family of techniques that has been extensively applied for limited adaptation data is transformation-based adaptation. In transformation-based adaptation we partition our parameter space in a set of classes, estimate a transform (usually linear) for each class and apply the ...

رابطه‌­ی شدت ترمولومینسانس براساس مدل سینتیک مرتبه‌­ی اول بدون در نظرگرفتن اثر فروکشی دمایی مورد استفاده قرار می‌­گیرد. این حالی است که به عنوان یک واقعیت فیزیکی باید روابط بیان‌­کننده‌­ی توجه گیرد. کار با نظر گرفتن برای اول، تابع جداسازی منحنی تابش جدیدی برحسب و دمای قله دست آمد. نحوی جدید حذف مشخصه‌­های دمایی، همان قبلی خود تبدیل می‌­شود. دزیمتر (400TLD-) :Mn2CaF بررسی گرفت. این‌­که ­شدت طبق ...

In this paper, we investigate the use of Multiple Background Models (M-BMs) in Speaker Verification (SV). We cluster the speakers using either their Vocal Tract Lengths (VTLs) or by using their speaker specific Maximum Likelihood Linear Regression (MLLR) super-vector, and build a separate Background Model (BM) for each such cluster. We show that the use of M-BMs provide improved performance whe...

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 di...