Evaluation of phoneme recognition through TDNN-OPGRU on Mandarin speech

This research expands past research on implementing the TDNN-OPGRU network for Automatic Phoneme Recognition on Dutch speech by implementing and testing the TDNN-OPGRU network on Mandarin speech. The goal of this research is to investigate the performance of the TDNN-OPGRU architecture when decoding phonemes in Mandarin prepared and spontaneous...

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...

Training and testing the TDNN-OPGRU acoustic model on English read and spontaneous speech

Automatic phoneme recognition (APR) is the process of recognizing phonemes (spoken sounds) in a recording of speech. It can be used for any application requiring fast and accurate transcription, i.e. a courthouse. This research creates such a model using the TDNN-OPGRU architecture and trains it on two datasets of recorded English speech - ...

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...