Automatic Speech Recognition for Documenting Endangered First Nations Languages

Automatic speech recognition (ASR) for low-resource languages is an active field of research. Over the past years with the advent of deep learning, impressive achievements have been reported using minimal resources. As many of the world’s languages are getting extinct every year, with every dying language we lose intellect, culture, values, and tradition which generally pass down for long generations. Linguists throughout the world have already initiated many projects on language documentation to preserve such endangered languages. Automatic speech recognition is a solution to accelerate the documentation process reducing the annotation time for field linguists as well as the overall cost of the project. A traditional speech recognizer is trained on thousands of hours of acoustic data and a phonetic dictionary that includes all words from the language. End-to-End ASR systems have shown dramatic improvement for major languages. Especially, recent advancement in self-supervised representation learning which takes advantage of large corpora of untranscribed speech data has become the state-of-the-art for speech recognition technology. However, for resource-constrained languages, the technology is not tested in depth. In this thesis, we explore both traditional methods of ASR and state-of-the-art end-to-end systems for modeling a critically endangered Athabascan language known as Upper Tanana. In our first approach, we investigate traditional models with a comparative study on feature selection and a performance comparison with deep hybrid models. With limited resources at our disposal, we build a working ASR system based on a grapheme-to-phoneme (G2P) phonetic dictionary. The acoustic model can also be used as a separate forced alignment tool for the automatic alignment of training data. The results show that the GMM-HMM methods outperform deep hybrid models in low-resource acoustic modeling. In our second approach, we propose using Domain-adapted Cross-lingual Speech Recognition (DA-XLSR) for an ASR system, developed over the wav2vec 2.0 framework that utilizes pretrained transformer models leveraging cross lingual data for building an acoustic representation. The proposed system uses a multistage transfer learning process in order to fine tune the final model. To supplement the limited data, we compile a data augmentation strategy combining six augmentation techniques. The speech model uses Connectionist Temporal Classification (CTC) for an alignment free training and does not require any pronunciation dictionary or language model. Experiments from the second approach demonstrate that it can outperform the best traditional or end-to-end models in terms of word error rate (WER) and produce a powerful utterance level transcription. On top of that, the augmentation strategy is tested on several end-to-end models, and it provides a consistent improvement in performance. While the best proposed model can currently reduce the WER significantly, it may still require further research to completely replace the need for human transcribers

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

International audienceAutomatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields

Speech recognition of south China languages based on federated learning and mathematical construction

As speech recognition technology continues to advance in sophistication and computer processing power, more and more recognition technologies are being integrated into a variety of software platforms, enabling intelligent speech processing. We create a comprehensive processing platform for multilingual resources used in business and security fields based on speech recognition and distributed processing technology. Based on the federated learning model, this study develops speech recognition and its mathematical model for languages in South China. It also creates a speech dataset for dialects in South China, which at present includes three dialects of Mandarin and Cantonese, Chaoshan and Hakka that are widely spoken in the Guangdong region. Additionally, it uses two data enhancement techniques—audio enhancement and spectrogram enhancement—for speech signal characteristics in order to address the issue of unequal label distribution in the dataset. With a macro-average F-value of 91.54% and when compared to earlier work in the field, experimental results show that this structure is combined with hyperbolic tangent activation function and spatial domain attention to propose a dialect classification model based on hybrid domain attention

Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion

Research on deep learning-powered voice conversion (VC) in speech-to-speech scenarios is getting increasingly popular. Although many of the works in the field of voice conversion share a common global pipeline, there is a considerable diversity in the underlying structures, methods, and neural sub-blocks used across research efforts. Thus, obtaining a comprehensive understanding of the reasons behind the choice of the different methods in the voice conversion pipeline can be challenging, and the actual hurdles in the proposed solutions are often unclear. To shed light on these aspects, this paper presents a scoping review that explores the use of deep learning in speech analysis, synthesis, and disentangled speech representation learning within modern voice conversion systems. We screened 621 publications from more than 38 different venues between the years 2017 and 2023, followed by an in-depth review of a final database consisting of 123 eligible studies. Based on the review, we summarise the most frequently used approaches to voice conversion based on deep learning and highlight common pitfalls within the community. Lastly, we condense the knowledge gathered, identify main challenges and provide recommendations for future research directions

Methods in Contemporary Linguistics

The present volume is a broad overview of methods and methodologies in linguistics, illustrated with examples from concrete research. It collects insights gained from a broad range of linguistic sub-disciplines, ranging from core disciplines to topics in cross-linguistic and language-internal diversity or to contributions towards language, space and society. Given its critical and innovative nature, the volume is a valuable source for students and researchers of a broad range of linguistic interests

Methods in Contemporary Linguistics

The present volume is a broad overview of methods and methodologies in linguistics, illustrated with examples from concrete research. It collects insights gained from a broad range of linguistic sub-disciplines, ranging from core disciplines to topics in cross-linguistic and language-internal diversity or to contributions towards language, space and society. Given its critical and innovative nature, the volume is a valuable source for students and researchers of a broad range of linguistic interests

Methods in Contemporary Linguistics

The present volume is a broad overview of methods and methodologies in linguistics, illustrated with examples from concrete research. It collects insights gained from a broad range of linguistic sub-disciplines, ranging from core disciplines to topics in cross-linguistic and language-internal diversity or to contributions towards language, space and society. Given its critical and innovative nature, the volume is a valuable source for students and researchers of a broad range of linguistic interests

Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization

Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which can not meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research