This document summarizes Maryam Najafian's research on acoustic model selection for recognizing regional accented speech. The research aims to use different automatic identification systems like phonotactic, i-vector, and ACCDIST-SVM features to select acoustic models for GMM-HMM and DNN-HMM based speech recognition of accents. The models are trained on British English and tested on 14 different accents from the ABI corpus. The document outlines the baseline identification systems, accent identification features, GMM-HMM adaptation techniques, comparisons of GMM-HMM and DNN-HMM, and the use of extra training and pre-training materials for DNN-HMMs.

1. Acoustic Model Selection for Recognition
of Regional Accented Speech
By Maryam Najafian
Supervisor Prof. Martin Russell
University of Birmingham
4th December 2015
Email: m.najafian@pgr.bham.ac.uk

2. Motivation
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3. Objectives
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• This research is concerned with automatic speech recognition (ASR) for accented
speech using a range of different AID systems for GMM-HMM and DNN-HMM
based acoustic model selection
• Trained on the SI training set (92 speakers, 7861 utterances) of the WSJCAM0
corpus of read British English speech
• Tested/adapted on ABI Corpus, 14 different accents (285 speakers)

4. Baseline AID System Design
Phonotactic
Accuracy : 80.65 %
I-vector
Accuracy :76.76%
ACCDIST-SVM
Accuracy: 95 %
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5. ACCDIST Accent ID feature space
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6. GMM-HMM: Unsupervised Adaptation
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7. 6/9
GMM-HMM: Speaker versus Accent Adaptation
Supervised speaker versus accent adaptation
Unsupervised speaker versus accent adaptation

8. DNN-HMM versus GMM-HMM
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9. DNN-HMM: Extra Training Material (ETM) &
Extra Pre-Training Material (EPM)
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10. 9/9
Summary and publications

11. Thank you for listening
Email: m.najafian@pgr.bham.ac.uk