Evaluating the Use of Pitch Shifting to Improve Automatic Speech Recognition Performance on Southern Dutch Accents

Building Automatic Speech Recognizers (ASRs) has been a challenge in languages with insufficiently sized corpora or data sets. A further large issue in language corpora is biases against regionally accented speech and other speaker attributes. There are some techniques to improve ASR performance and reduce biases in these corpora, known as data...

Improving Northern Regional Dutch Speech Recognition by Adapting Perturbation-based Data Augmentation

Automatic speech recognition (ASR) does not perform equally well on every speaker. There is bias against many attributes, including accent. To train Dutch ASR, there exists CGN(Corpus Gesproken Nederlands) and as an extension, the JASMIN corpus with annotated accented data. This paper focuses on improving ASR performance for NRAD (Northern...

Evaluating the Use of Frequency Masking on a Hybrid Automatic Speech Recognizer for Transitional Dutch Accent of JASMIN-CGN Corpus

There are many experiments conducted with Automatic Speech Recognition (ASR) systems, but many either focus on specific speaker categories or on a language in general. Therefore, bias could occur in such ASR systems towards different genders, age groups, or dialects. But, to analyze and reduce bias, the models require significant amounts of data...

Evaluating the Effect of SpecSwap for Purposes of Improving WER Performance of the Western Dutch Region Using the JASMIN-CGN Dataset

A problem prevalent in many modern-day Automatic Speech Recognition (ASR) systems is the presence of bias and its reduction. Bias can be observed when an ASR system performs worse on a subset of its speakers compared to the rest rather than having the same overall generalization for everyone. This can be seen by using Word Error Rates (WER) as a...

Signal-processing of audio for speech-recognition

The transcription of voice using neural networks is a technique that deserves attention, as speech assistants are becoming increasingly popular. Neural networks have often difficulty with determining the differences between a talking person and noise. Humans have a much better understanding of this and could possibly apply their knowledge of the...

Assessing the performance of the TDNN-BLSTM architecture for phoneme recognition of English speech

This research studies the Projected Bidirectional Long Short-Term Memory Time Delayed Neural Network (TDNN-BLSTM) model for English phoneme recognition. It contributes to the field of phoneme recognition by analyzing the performance of the TDNN-BLSTM model based on the TIMIT corpus and the Buckeye corpus, respectively containing read speech and...

Evaluating the performance of TDNN-BLSTM on Mandarin read and spontaneous speech

A limitation of current ASR systems is the so-called out-of-vocabulary words. The solution to overcome this limitation is to use APR systems. Previous research on Dutch APR systems identified Time Delayed Bidirectional Long-Short Term Memory Neural Network (TDNN-BLSTM) as one of best performing state-of-the-art NN architecture for PR. The goal...