Название: Analysis and Application of Natural Language and Speech ProcessingАвтор: Mourad Abbas
Издательство: Springer
Год: 2023
Страниц: 217
Язык: английский
Формат: pdf (true), epub
Размер: 18.9 MB
This book presents recent advances in Natural Language Processing (NLP) and speech technology, a topic attracting increasing interest in a variety of fields through its myriad applications, such as the demand for speech guided touchless technology. The authors present results of recent experimental research that provides contributions and solutions to different issues related to speech technology and speech in industry. Technologies include Natural Language Processing, automatic speech recognition (for under-resourced dialects) and speech synthesis that are useful for applications such as intelligent virtual assistants, among others. Applications cover areas such as sentiment analysis and opinion mining, and language modelling. This book is relevant for anyone interested in the latest in language and speech technology.
The increased access to powerful processors has made possible significant progress in Natural Language Processing (NLP). We find more research in NLP targeting diverse spectrum of major industries that use voice recognition, text-to-speech (TTS) solutions, speech translation, natural language understanding (NLU), and many other applications and techniques related to these areas.
This book presents the latest research related to Natural Language Processing and speech technology and sheds light on the main topics for readers interested in this field. For TTS and automatic speech recognition, it is demonstrated how to explore transfer learning in order to generate speech in other voices from TTS of a specific language, and to improve speech recognition for non-native English. Language resources are the cornerstone for building high-quality systems; however, some languages, are considered under-resourced compared to English. In addition, the readers of this book will discover conceptions and solutions for other NLP issues such as language modeling, question answering, dialog systems, and sentence embeddings.
Although non-native English speakers (L2) outnumber native English speakers (L1), major challenges contribute to a gap between performance of automatic speech recognition (ASR) systems on L2 speech. This is mainly due to influence of L1 pronunciation on the learned language and lack of annotated L2 speech data on which ASR systems can be trained. To meet these challenges, previous work has generally followed two distinct approaches. The first is to make L2 speech representations more closely match those of L1 speech. The second approach leverages L2 speech data to improve model robustness. Due to L2 data scarcity, this second approach necessitates employment of transfer learning or domain adaptation. State-of-the-art ASR models based on self-supervised pre-training such as wav2vec and wav2vec 2.0 offer a tantalizing starting point for applying the transfer learning approach we list above, especially due to their strong performance of self-trained wav2vec 2.0 models on ASR in low-resource settings even without a language model. However, challenges remain in identifying how best to apply models such as wav2vec 2.0 in L2 fine-tuning scenarios.
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