Large Vocabulary Continuous Speech Recognition

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Introduction

Large Vocabulary Continuous Speech Recognition(LVCSR), stands as a sophisticated technology within the domain of Automatic Speech Recognition (ASR). LVCSR specifically focuses on the recognition of a sequence of words from a vast and diverse vocabulary without information of the word boundaries.[1] LVCSR system is composed of four indispensable components: Front-End Processing, Acoustic Model, Language Model, and Search & System Combination.[2] Collectively, these components work harmoniously to transform spoken speech signals into coherent word sequences. With the advancement of deep learning, LVCSR has witnessed a substantial enhancement in accuracy. This breakthrough has broadened the applicability of LVCSR across various industries and applications, making it an indispensable tool in the realm of speech recognition technology.

Historical Context

The concept of Automatic Speech Recognition began to take shape in the 1950s and 1960s, with early research focused on constructing recognition systems for isolated word speech using rudimentary techniques.

In the 1970s, a groundbreaking development occurred when Hidden Markov Models (HMMs) were successfully applied to continuous speech recognition systems. This pivotal moment marked the evolution of speech recognition from basic pattern matching approaches to sophisticated statistical probability models.

The 1980s witnessed the emergence of LVCSR. To achieve continuous speech recognition, innovations like Linear Predictive Coding (LPC) and the incorporation of statistical language modeling significantly enhanced the accuracy and resilience of LVCSR systems.

Entering the 1990s, LVCSR applications gained momentum in the market, finding increasing utility in transcription services, customer support applications, and supplementary tools.

In the 21st century, fueled by advancements in artificial intelligence and deep learning, acoustic models continued to evolve. Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) gradually assumed a dominant role in the LVCSR field, further elevating the performance and precision of speech recognition. This widespread adoption paved the way for applications such as real-time transcription and voice commands. Today, research in the LVCSR domain extends across diverse sectors, including healthcare, automotive, and education, propelling innovation in human-computer interaction and assistive technologies.

Key Innovations

Development of End-to-End Models

All the time, LVCSR is developing in a more End-to-End Models. The traditional LVCSR usually includes various modules, for instance, acoustic models, language models, and pronunciation dictionaries. However, recently decade, to simplify the process and improve LVCSR efficiency, experts are focusing on how to use a neural network to couple all these modules into a unite model.

This approach utilizes a Recurrent Neural Network (RNN) model for phoneme recognition, which is the process of converting spoken sounds into text. A distinctive feature of this novel method is its ability to perform this conversion without the need for complex alignment operations, allowing for the direct generation of the desired textual output. Additionally, it is capable of operating in real-time scenarios where there are stringent timing requirements.

The foundation of this method is an extension of a neural machine translation model, which bears similarities to certain existing speech recognition techniques. However, what sets it apart is its capacity to calculate scores for all positions within both the input and output sequences, subsequently employing these scores to aid in the recognition process. An innovative aspect of this approach is the explicit utilization of these scores for alignment purposes, facilitating the generation of accurate textual representations. Furthermore, the decoder state in this model incorporates information regarding prior alignment choices, enhancing the precision of speech understanding.

A key advantage of this method is its ability to perform decoding in an almost deterministic manner, making it suitable for real-time speech recognition without the need for computationally intensive procedures. This performance characteristic suggests the feasibility of extending the method to large-vocabulary speech recognition systems. It also opens up the possibility of directly searching for the most probable word sequences, as opposed to conducting searches at the phoneme or frame level, as is traditionally done in Hidden Markov Model (HMM)-based hybrid systems. This feature contributes significantly to improving the speed and accuracy of speech recognition.[3]

Multimodal integration in speech recognition

Multimodal integration in speech recognition refers to the process of combining information from multiple sensory modalities, such as audio (speech signals) and visual (lip movements or facial expressions), to improve the accuracy and robustness of speech recognition systems.

In traditional speech recognition, the system relies solely on audio input to transcribe spoken words.  However, this approach can be limited in noisy environments or when there are variations in speech articulation.  Multimodal integration seeks to address these limitations by incorporating additional sources of information, such as visual cues from the speaker's mouth movements.

The idea behind multimodal integration is that different modalities can provide complementary information that helps disambiguate spoken words.  For example, lip movements can provide information about the shape of the mouth and the position of the tongue, which can be useful for disambiguating similar-sounding words.[4]

Impact

Over the past decade or so, several advances have been made to the design of modern largevocabulary continuous speech recognition (LVCSR) systems to the point where their application has broadened from early speakerdependent dictation systems to speaker-independent automatic broadcast news transcription and indexing, lectures and meetings transcription, conversational telephone speech transcription, open-domain voice search, medical and legal speech recognition, and call center applications, to name a few. The commercial success of these systems is an impressive testimony to how far research in LVCSR has come.[5] LVCSR technology has significantly advanced the capabilities of speech recognition, enabling a wide range of applications and benefiting numerous sectors.

Improved Accuracy and Enhanced User Experience

  • LVCSR has substantially improved the accuracy of speech recognition systems. It allows for the transcription of continuous speech and recognition of a vast vocabulary, resulting in more precise and natural language understanding. On the other side, LVCSR has made voice interactions more user-friendly and intuitive. Users can communicate naturally and expect more accurate responses from speech-based systems.
  • The higher accuracy of LVCSR has made it more practical and reliable for various applications, such as voice assistants like Siri, Google Assistant, and Alexa, transcription services like Riverside, Ottor, and customer support.

Automation and Accessibility

  • LVCSR technology enables automation in various sectors, reducing the need for manual intervention and streamlining processes, and improving accessibility for individuals with disabilities, especially those with hearing impairments or speech disabilities.
  • In customer service, IVR systems use LVCSR to handle inquiries efficiently. In healthcare, LVCSR aids in medical transcription like Deepscribe, Nuance DAX, and in finance, it powers voice-activated banking services. Voice-to-text conversion tools based on LVCSR enable people with disabilities to communicate effectively through text-based interfaces, opening up new opportunities for them.

Research Opportunities and Diverse Applications

  • LVCSR technology has spurred research in NLP, AI, and related fields, offering opportunities for innovation and development. Applications in a wide array of industries, including healthcare, automotive, education, entertainment, finance, and security have been developed driven by LVCSR technology .
  • Ongoing research contributes to making speech recognition systems smarter, capable of handling complex conversations, and understanding nuances better.It is integral to voice-controlled devices, language translation services, content indexing, educational tools, voice analytics, and security/authentication systems.

Future research

Emotion recognition and speech generation:

Combining emotion recognition technology with speech recognition to better understand and synthesize speech with emotional color. This has potential value in applications such as virtual assistants and automated telephone customer service.[6]

Language modeling

Language modeling is investigated by experts techniques specifically for conversational speech. For example, discriminative LM training methods and LMs that capitalize on conversational speech patterns. Much of the SRI Language Modeling Toolkit was developed as a by-product of LVCSR research, and SRI often provides language modeling support for other sites in the LVCSR community.[7]

Continuous adaptive and incremental learning:

Develop LVCSR systems with continuous adaptive capabilities that can adapt to changing environments and user needs, as well as enable incremental learning that can continuously improve performance while constantly accumulating data.[8]

Contribution of ChatGPT

The foundation of this wiki page was established through an in-depth exploration of relevant academic research publications available online. Contributors conducted extensive research to gather accurate and up-to-date information regarding LVCSR technology, its evolution, and its real-world applications.

ChatGPT helped in elevating the language and style of the content to a professional standard. It was utilized to refine sentence structures, improve vocabulary usage, and enhance overall readability. Apart from that, ChatGPT assisted in structuring the content logically. It suggested headings, subheadings, and bullet points to enhance organization, ensuring that topics flowed seamlessly throughout the page. This aided in presenting complex concepts in a comprehensible manner.

Prior to publication, contributors meticulously fact-checked and validated all information provided by ChatGPT to ensure that the content was not only well-written but also accurate and reliable.

References

To insert a reference, type <ref> and paste the source your exported from Zotero (or whatever reference manager you're using) in the pop up box which appears. Make sure links in citations are clickable using proper formatting. Once you do this, a footnote will appear.[9] and a reference comes at the end automatically. Please use this method to cite for Wiki articles only, not for your thesis.

  1. ↑ Mitankin, P., Mihov, S. and Tinchev, T. (2009) ‘Large Vocabulary Continuous Speech Recognition for Bulgarian’.
  2. ↑ Saon, G., & Chien, J.-T. (2012). Large-Vocabulary Continuous Speech Recognition Systems: A Look at Some Recent Advances. IEEE Signal Processing Magazine, 29(6), 18–33. https://doi.org/10.1109/MSP.2012.2197156
  3. ↑ Chorowski, J., Bahdanau, D., Cho, K., & Bengio, Y. (2014). End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results (arXiv:1412.1602;). arXiv. http://arxiv.org/abs/1412.1602
  4. ↑ Conneau, A., Lample, G., Ranzato, M., Denoyer, L., & Jégou, H. (2018). Word Translation Without Parallel Data (arXiv:1710.04087). arXiv. http://arxiv.org/abs/1710.04087
  5. ↑ Saon, G., & Chien, J.-T. (2012). Large-Vocabulary Continuous Speech Recognition Systems: A Look at Some Recent Advances. IEEE Signal Processing Magazine, 29(6), 18–33. doi:10.1109/msp.2012.2197156
  6. ↑ Han, K., Yu, D., & Tashev, I. (2014). Speech Emotion Recognition Using Deep Neural Network and Extreme Learning Machine.
  7. ↑ Stolcke, A., Zheng, J., Wang, W., & Abrash, V. SRILM at Sixteen: Update and Outlook.
  8. ↑ Feng, S., Kudina, O., Halpern, B. M., & Scharenborg, O. (2021). Quantifying Bias in Automatic Speech Recognition (arXiv:2103.15122). arXiv. http://arxiv.org/abs/2103.15122
  9. ↑ Glantz, Richard "SHOEBOX: a personal file handling system for textual data." In Proceedings of the November 17-19, 1970, Fall Joint Computer Conference 1970. 535-545. [1]


group members: Yan Liao, Jingxuan Yue, Chenyu Li

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