Georg Stemmer

Integration of Heteroscedastic Linear Discriminant Analysis (HLDA) Into Adaptive Training

The paper investigates the integration of heteroscedastic linear discriminant analysis (HLDA) into adaptively trained speech recognizers. Two different approaches are compared: the first is a variant of CMLLR-SAT, the second is based on our previously introduced method constrained maximum-likelihood speaker normalization (CMLSN). For the latter both HLDA projection and speaker-specific transformations for normalization are estimated w.r.t. a set of simple target-models. It is investigated if additional robustness can be achieved by estimating HLDA on normalized data. Experimental results are provided for a broadcast news task and a collection of parliamentary speeches. We show that the proposed methods lead to relative reductions in word error rate (WER) of 8% over an adapted baseline system that already includes an HLDA transform. The best performance for both tasks is achieved for the algorithm that is based on CMLSN. When compared to the combination of HLDA and CMLLR-SAT, this method leads to a considerable reduction in computational effort and to a significantly lower WER

Accented Indian english ASR: Some early results

The problem of the effect of accent on the performance of Automatic Speech Recognition (ASR) systems is well known. In this paper, we study the effect of accent variability on the performance of the Indian English ASR task. We evaluate the test vocabularies on HMMs trained on (a) Accent specific training data (b) Accent pooled training data which combines all the accent specific training data (c) Accent pooled training data of reduced size matching the size of the accent specific training data. We demonstrate that the accent pooled training set performs the best on phonetically rich isolated word recognition task. But the accent specific HMMs perform better than the reduced accent pooled HMMs, indicating a possible approach of using a first stage accent identification to choose the correct accent trained HMMs for further recognition.