Improving recognition performance by modelling pronunciation variation.

This paper describes a method for improving the performance of a continuous speech recognizer by modelling pronunciation variation. Although the improvements obtained with this method are small, they are in line with those reported by other authors. A series of experiments was carried out to model pronunciation variation. In the first set of experiments word internal pronunciation variation was modelled by applying a set of four phonological rules to the words in the lexicon. In the second set of experiments, variation across word boundaries was also modelled. The results obtained with both methods are presented in detail. Furthermore, statistics are given on the application of the four phonological rules on the training database. We will explain why the improvements obtained with this method are small and how we intend to increase the improvements in our future research

Two Automatic Approaches for Analyzing Connected Speech Processes in Dutch

This paper describes two automatic approaches used to study connected speech processes (CSPs) in Dutch. The first approach was from a linguistic point of view - the top-down method. This method can be used for verification of hypotheses about CSPs. The second approach - the bottom-up method -uses a constrained phone recognizer to generate phone transcriptions. An alignment was carried out between the two transcriptions and a reference transcription. A comparison between the two methods showed that 68% agreement was achieved on the CSPs. Although phone accuracy is only 63%, the bottom-up approach is useful for studying CSPs. From the data generated using the bottom-up method, indications of which CSPs are present in the material can be found. These indications can be used to generate hypotheses which can then be tested using the top-down method

Comparison between expert listeners and continuous speech recognizers in selecting pronunciation variants.

In this paper, the performance of an automatic transcription tool is evaluated. The transcription tool is a continuous speech recognizer (CSR) which can be used to select pronunciation variants (i.e. detect insertions and deletions of phones). The performance of the CSR was compared to a reference transcription based on the judgments of expert listeners. We investigated to what extent the degree of agreement between the listeners and the CSR was affected by employing various sets of phone models (PMs). Overall, the PMs perform more similarly to the listeners when pronunciation variation is modeled. However, the various sets of PMs lead to different results for insertion and deletion processes. Furthermore, we found that to a certain degree, word error rates can be used to predict which set of PMs to use in the transcription tool

On automatic phonetic transcription quality: Lower word error rates do not guarantee better transcriptions

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Using Dutch phonological rules to model pronunciation variation in ASR

In this paper, we describe how the performance of a continuous speech recognizer for Dutch has been improved by modeling within-word and cross-word pronunciation variation. Within-word variants were automatically generated by applying five phonological rules to the words in the lexicon. Cross-word pronunciation variation was modeled by adding multi-words and their variants to the lexicon. The best results were obtained when the cross-word method was combined with the within-word method: a relative improvement of 8.8 % in the WER was found compared to baseline system performance. We also describe an error analysis that was carried out to investigate whether rules in isolation can predict the performance of rules in combination. 1

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data-driven method for modeling pronunciation variatio