Speech Recognition Technology and Applications

Computer Science, Technologyand Applications
Speech Recognition Technologyand Applications
Contents
Preface
Chapter 1Building an Automatic Speech RecognitionSystem for a Low-Resource Language
	Abstract
	1. Introduction
		1.1. Romanian as a Low-Resource Language
	2. State-of-the-Art Architectures
		2.1. HMM-GMMBased Architectures
		2.2. Deep Neural Networks Architectures
		2.3. Hybrid Architectures
		2.4. Language Models
	3. Method
		3.1. Corpora
		3.2. Automatic Grapheme-to-Phoneme Conversion
		3.3. Language Models
		3.4. Data Augmentation
		3.5. Speech-to-Text Architectures for Romanian
			3.5.1. CMUSphinx
			3.5.2. DeepSpeech
			3.5.3. DeepSpeech 2
			3.5.4. Kaldi
		3.6. Replicable Experiments with Containerization
	4. Results
		4.1. CMUSphinx
		4.2. DeepSpeech
		4.3. Kaldi
		4.4. Data Augmentation and SpecAugment
	5. Discussion
		5.1. CMUSphinx
		5.2. DeepSpeech
		5.3. Kaldi
	Conclusion and FutureWork
	Acknowledgment
	References
Chapter 2Self-Supervised Pre-Training in SpeechRecognition Systems
	Abstract
	1. Introduction
	2. Contrastive Representation Learning
		2.1. Training Objectives
		2.2. Essential Components
	3. Pre-Trained ASR Architectures
		3.1. Wav2Vec
		3.2. VQ-Wav2Vec
		3.3. Wav2Vec2
	4. Comparison with Non-Pre-TrainedModels
		4.1. Dataset
		4.2. Baseline Models
		4.3. Pre-TrainedWav2Vec2 Models
		4.4. Experimental Setup
		4.5. Results
	5. RELATE Integration
	Conclusion
	References
Chapter 3The Impact of Speech RecognitionPerformance on Human-ComputerInteraction
	Abstract
	1. Introduction
	2. Architecture of a Speech-Based Dialogue System
	3. Implementation Details
		3.1. Automatic Speech Recognition
		3.2. DialogueManager
		3.3. Text-to-Speech
	4. ASR Enhancements Leading to IncreasedPerformance of the Overall System
		4.1. End-to-End Neural ASR System
		4.2. Fine-Tuning the ASR System with Domain-Specific Data
	5. Impact of ASR Enhancements
		5.1. Evaluation of RDM with a Fine-Tuned ASR System
		5.2. Overall System Response Time
	Conclusion
	References
Chapter 4The Role of Automatic Speech RecognitionSystems in Developing Medical Applications
	Abstract
	1. Introduction
	2. General Overview of ASR
	3. NLP Applications in Medical Domain
		3.1. Named Entity Recognition
		3.2. Classification
		3.3. Summarization
	4. ASR Applications in Medical Domain
		4.1. Digital Scribes for Medical Domain
			4.1.1. Challenges of Developing Digital Scribes for theMedical Domain
		4.2. Software and Platforms with ASR-Based Capabilities intheMedical Domain
			4.2.1. Case Study: AmazonMedical
				• Amazon Transcribe Medical
				• Amazon Comprehend Medical
		4.3. ASR and Vocal Biomarkers
		4.4. Medical IOT and ASR
	Conclusion
	References
Chapter 5Punctuation Recovery for RomanianTranscribed Documents
	Abstract
	1. Introduction
	2. Punctuation in Romanian Language
	3. Corpora and Resources
	4. Algorithms
	5. Results
	Conclusion
	References
Chapter 6Linguistic Linked Open Datafor Speech Processing
	Abstract
	1. Introduction
	2. Linguistic Linked Open Data
	3. Romanian Resources as Linguistic Linked OpenData
	4. LLOD Resources for Speech Processing
	5. Romanian LLOD Resources for SpeechProcessing
		5.1. The RoLEX Lexicon
		5.2. The RTASC Corpus
	6. ExploitingMultiple Resources for AdvancedUsage Scenarios
	Conclusion
	References
Chapter 7Transformer-Based RomanianText-to-Speech System Using BooleanMasking for Improved Prosody
	Abstract
	1. Introduction
	2. RelatedWork
		2.1. Deep Neural Models for Speech Synthesis
		2.2. Speech Synthesis for the Romanian Language
	3. Datasets for the Romanian Language
		3.1. Existing Datasets
		3.2. Introducing a New Male Voice Dataset - RSS-Alex
	4. FastSpeech TTS with BooleanMasking
	5. Experiments
	6. Results
	Conclusion
	FutureWork
	Acknowledgments
	References
About the Editor
About the Contributors
Index
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