Vocal tract normalization (VTN) is a widely used speaker normalization technique which reduces the effect of different lengths of the human vocal tract and results in an improved recognition accuracy of automatic speech recognition systems. We show that VTN results in a linear transformation in the cepstral domain, which so far have been considered as independent approaches of speaker normalization. We are now able to compute the Jacobian determinant of the transformation matrix, which allows the normalization of the probability distributions used in speaker-normalization for automatic speech recognition. We show that VTN can be viewed as a special case of Maximum Likelihood Linear Regression (MLLR). Consequently, we can explain previous experimental results that improvements obtained by VTN and subsequent MLLR are not additive in some cases. For three typical warping functions the transformation matrix is calculated analytically and we show that the matrices are diagonal dominant and thus can be approximated by quindiagonal matrices.

We show that vocal tract normalization (VTN) frequency warping results in a linear transformation in the cepstral domain.For the special case of a piece-wise linear warping function, the transformation matrix is analytically calculated.This approach enables us to compute the Jacobian determinant of the transformation matrix, which allows the normalization of the probability distributions used in speaker-normalization for automatic speech recognition.

We present a method to derive Mel-frequency cepstral coefficients directly from the power spectrum of a speech signal. We show that omitting the filterbank in signal analysis does not affect the word error rate. The presented approach simplifies the speech recognizers front end by merging subsequent signal analysis steps into a single one. It avoids possible interpolation and discretization problems and results in a compact implementation. We show that frequency warping schemes like vocal tract normalization can be integrated easily in our concept without additional computational efforts. Recognition test results obtained with the RWTH large vocabulary speech recognition system are presented for two different corpora: The German VerbMobil II dev99 corpus, and the English North American Business News 94 20k development corpus.

Automatic recognition of conversational speech tends to have higher word error rates (WER) than read speech. Improvements gained from unsupervised speaker adaptation methods like Maximum Likelihood Linear Regression (MLLR) [1] are reduced because of their sensitivity to recognition errors in the first pass. We show that a more detailed modeling of adaptation classes and the use of confidence measures improve the adaptation performance. We present experimental results on the VERBMOBIL task, a German conversational speech corpus.

This paper contains a description of the Philips/RWTH 1998 HUB4 system which has been build in a joint eort of Philips Research Laboratories Aachen and Aachen University o f T echnology.We will focus our discussion on recent improvements compared to the original 1997 HUB4 system and evaluate them on the HUB4'97 evaluation data.The paper will deal with 1. a rough system overview including feature extraction, acoustic training, audio stream segmentation, and decoding 2. log-linear interpolation of distance-language models, 3. and the integration of various acoustic and language models via Discriminative Model Combination (DMC).The performance of the described system is 23% (relative) better than the performance of the 1997 Philips HUB4 system.A w ord error rate of 17.9% was achieved on the 1997 HUB4 evaluation set, compared to 23.5% using the original 1997 system.

In this paper we present a method for automatically detecting erroneous training scripts for speech corpora like Broadcast News and Switchboard.Based on the Hub-4 task we will report on the performance of error detection with the proposed method and investigate the effects of both manually and automatically cleaned training corpora on the performance of the RWTH speech recognition system.Our approach uses a forced Viterbi alignment on the training data and evaluates different transcription quality measures.The following three criteria proved to be useful to automatically detect most transcription errors: the difference between the final Viterbi alignment HMM state and the last state according to the transcriptions the normalized acoustic word scores the location of the boundary between adjacent segments obtained by forced alignment With manually corrected scripts we achieved a WER reduction on the 1996 HUB-4 eval.corpus.The recognizer's performance improved mainly on clean planned speech segments.Whereas the improvements were minor on these hand-transcribed training data, automatic training script verification will become more important for automatically transcribed new speech corpora.