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Automatic Speech Recognition (ASR) 2021-22: Lectures

Lectures take place on Mondays at 13:10 and Thursdays at 15.10, starting Monday 17 January. Future lecture topics are subject to change.

1. Monday 17 January 2022. Introduction to Speech Recognition
   Slides
   Reading: J&M: chapter 7, section 9.1; R&H review chapter (sec 1).
    
2. Thursday 20 January 2022. Speech Signal Analysis 1
   Slides (updated 24 January)
   Reading: O'Shaughnessy (2000), Speech Communications: Human and Machine, chapter 2; J&M: Sec 9.3; Paul Taylor (2009), Text-to-Speech Synthesis: Ch 10 and Ch 12.
   SparkNG MATLAB realtime/interactive tools for speech science research and education
    
3. Monday 24 January 2022. Speech Signal Analysis 2
   Slides (updated 31 January)
   Reading: O'Shaughnessy (2000), Speech Communications: Human and Machine, chapter 3-4
    
4. Thursday 27 January 2022. Hidden Markov Models
   Slides (updated 31 January)
   Reading: Rabiner & Juang (1986) Tutorial.; J&M: Secs 6.1-6.5, 9.2, 9.4; R&H review chapter (sec 2.1, 2.2);
    
5. Monday 31 January 2022. Training HMMs
   Slides
   Reading: J&M: Sec 9.7, G&Y review (sections 1, 2.1, 2.2); (J&M: Secs 9.5, 9.6, 9.8 for introduction to decoding).
    
6. Thursday 3 February 2022. Gaussian mixture models
   Slides
   Reading: R&H review chapter (sec 2.2)
    
7. Monday 7 February 2022. HMM acoustic modelling 3: Context-dependent phone modelling
   Slides
   Reading: J&M: Sec 10.3; R&H review chapter (sec 2.3); Young (2008).
    
8. Thursday 10 February 2022. Large vocabulary ASR
   Slides
   Reading: Ortmanns & Ney
    
9. Monday 14 February 2022. ASR with WFSTs
   Slides
   Reading: Mohri et al (2008), Speech recognition with weighted finite-state transducers, in Springer Handbook of Speech Processing (sections 1 and 2)
    
10. Thursday 17 February 2022. Neural network acoustic models 1: Introduction
    Slides
    Reading: Jurafsky and Martin (draft 3rd edition), chapter 7 (secs 7.1 - 7.4)
    Background Reading: M Nielsen (2014), Neural networks and deep learning - chapter 1 (introduction), chapter 2 (back-propagation algorithm), chapter 3 (the parts on cross-entropy and softmax).
     
    Monday 21 - Friday 25 February 2022.
    NO LECTURES OR LABS - FLEXIBLE LEARNING WEEK.
     
    
11. Monday 28 February 2022. Neural network acoustic models 2: Hybrid HMM/DNN systems
    Slides
    Background Reading: Morgan and Bourlard (May 1995). Continuous speech recognition: Introduction to the hybrid HMM/connectionist approach, IEEE Signal Processing Mag., 12(3):24-42
    Mohamed et al (2012). Understanding how deep belief networks perform acoustic modelling, ICASSP-2012.
     
12. Thursday 3 March 2022. Neural Networks for Acoustic Modelling 3: DNN architectures
    Slides
    Reading: Maas et al (2017), Building DNN acoustic models for large vocabulary speech recognition Computer Speech and Language, 41:195-213.
    Background reading: Peddinti et al (2015). A time delay neural network architecture for efficient modeling of long temporal contexts, Interspeech-2015
    Graves et al (2013), Hybrid speech recognition with deep bidirectional LSTM, ASRU-2013.
     
13. Monday 7 March 2022. Speaker Adaptation
    Slides
    Reading: G&Y review, sec. 5
    Woodland (2001), Speaker adaptation for continuous density HMMs: A review, ISCA Workshop on Adaptation Methods for Speech Recognition
    Bell et al (2021), Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview , IEEE Open Journal of Signal Processing, Vol 2:33-36.
     
14. Thursday 10 March 2022 Discriminative training
    Slides
    Reading: Sec 27.3.1 of Young (2008), HMMs and Related Speech Recognition Technologies.
     
15. Monday 14 March 2022. Multilingual and low-resource speech recognition
    Slides (updated 14 March)
    Reading: Besaciera et al (2014), Automatic speech recognition for under-resourced languages: A survey, Speech Communication, 56:85--100.
    Huang et al (2013). Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers, ICASSP-2013.
     
16. Thursday 17 March 2022. End-to-end systems 1: CTC
    Slides
    Reading: A Hannun et al (2014), Deep Speech: Scaling up end-to-end speech recognition, ArXiV:1412.5567.
    A Hannun (2017), Sequence Modeling with CTC, Distill.
    Background Reading: Y Miao et al (2015), EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding, ASRU-2105.
    A Maas et al (2015). Lexicon-free conversational speech recognition with neural networks, NAACL HLT 2015.
     
17. Monday 21 March 2022. End-to-end systems 2: Encoder-decoder models
    Slides
    Reading: W Chan et al (2015), Listen, attend and spell: A neural network for large vocabulary conversational speech recognitionICASSP.
    R Prabhavalkar et al (2017), A Comparison of Sequence-to-Sequence Models for Speech Recognition, Interspeech.
    Background Reading: C-C Chiu et al (2018), State-of-the-art sequence recognition with sequence-to-sequence models, ICASSP.
    S Watanabe et al (2017), Hybrid CTC/Attention Architecture for End-to-End Speech Recognition, IEEE STSP, 11:1240--1252.
     
18. Thursday 24 March 2022. Guest lecture: Unsupervised raw waveform modelling
    Slides
    Background Reading: A van den Ooord et al (2018), Representation learning with contrastive predictive coding
    S Schneider et al (2019), wav2vec: Unsupervised pre-training for speech recognition, Interspeech.
     
19. Date to be confirmed. Revision lecture – questions and answers
    
     

Reading

Textbook (essential)

• J&M: Daniel Jurafsky and James H. Martin (2008). Speech and Language Processing, Pearson Education (2nd edition).

Review and Tutorial Articles

• G&Y: MJF Gales and SJ Young (2007). The Application of Hidden Markov Models in Speech Recognition, Foundations and Trends in Signal Processing, 1 (3), 195-304.
• S Young (1996). A review of large-vocabulary continuous-speech recognition, IEEE Signal Processing Magazine 13 (5), 45-57.
• R&H:S Renals and T Hain (2010). Speech Recognition, in Computational Linguistics and Natural Language Processing Handbook, A Clark, C Fox and S Lappin (eds.), Blackwells, chapter 12, 299-332.
• G Hinton et al (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, IEEE Signal Processing Magazine, 29(6):82-97.
• S Young (2008). HMMs and Related Speech Recognition Technologies, in Springer Handbook of Speech Processing, J Benesty, MM Sondhi and Y Huang (eds), chapter 27, 539-557.

Other supplementary materials

• In case you need more introductory articles on speech signal analysis (Lectures 2 and 3):
  Daniel P.W. Ellis, "An introduction to signal processing for speech", Chapter 22 in The Handbook of Phonetic Science, 2nd ed., ed. Hardcastle, Laver, and Gibbon. pp. 757-780, Blackwell, 2008.
• Speech.zone by Prof Simon King at the University of Edinburgh.

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Last updated: 2022/03/24 12:40:29UTC