Technology will move toward real-time speech
recognition and instant response to support faster and
more natural human-computer interaction, such as
real-time translation, real-time response from voice
assistants, and more.
In the future, with the continuous progress and
deepening of neural network technology, researchers
can foresee the further development of speech
recognition in multi-language, multi-modal (such as
speech and image combination), adaptive
environment and other aspects. At the same time, the
structure optimization of neural network model and
the improvement of computing power will further
improve the performance and universality of speech
recognition system. With the process of globalization,
speech recognition technology will further enhance
the support for multiple languages and dialects, thus
promoting the application and popularization of the
world.
This paper discusses the application of several
common neural networks, compares the advantages
and disadvantages of various common methods,
compares and discusses them, and obtains the
following improvement schemes:
Future research may focus more on how to
develop high-performance speech recognition
systems in low-resource environments. Techniques
such as semi-supervised learning, transfer learning,
and multitasking learning are likely to play an
important role in this area.
Improving the robustness and adaptability of
speech recognition systems is still an important
direction. Researchers may continue to explore
training and optimization strategies on diverse data
(e.g., noise data, multi-dialect data).
The development of adaptive systems, such as
those that can automatically adjust parameters to
different environments or speakers, may lead to
breakthroughs in the future.
Combining speech recognition with other modes
(e.g., vision, text) is a direction worth exploring. By
integrating multimodal information, future systems
may be able to provide more accurate and robust
speech recognition services. For example, the
combination of lip-reading information in video with
voice data is expected to improve recognition rates in
noisy environments.
5 CONCLUSION
In this paper, through experimental comparison, it is
found that neural networks DNN have shown
significant advantages in the study of speech
recognition, and become the core technology of many
modern speech recognition systems. Neural network-
based methods, especially CNNS and RNNS, are
extremely capable of processing the spatiotemporal
properties of speech signals. They can accurately
extract features and classify them in complex audio
environments, thus improving the accuracy and
robustness of speech recognition. In particular, deep
learning methods significantly improve the
performance of speech recognition, making the
system more stable in a variety of environments.
Neural networks can learn large amounts of data and
automatically adapt to different speech variants,
accents and background noise, with stronger
generalization ability. While traditional speech
recognition systems often rely on multiple
independent components (such as feature extraction,
acoustic models, language models, etc.), the end-to-
end system based on neural networks simplifies these
steps and can be jointly optimized through
backpropagation to further improve the overall effect.
The application of neural networks in speech
recognition has made remarkable progress in this
technology, especially the rise of end-to-end models,
marking the speech recognition technology has
entered a new era. However, there are still many
challenges to overcome in the future, especially in
terms of application, diversity and robustness in low-
resource environments. Through continued research
and exploration, speech recognition technology is
expected to achieve breakthroughs in more areas, and
provide more accurate and reliable services to a wider
range of users.
REFERENCES
Shi, Y., 2017. Optimization and Design of speech
recognition Scheme based on recurrent neural network.
Beijing Jiaotong University.
Dai, W., Liu, H., 2022. Chinese speech recognition based
on neural network. Journal of Sichuan Normal
University.
Zhao, Z. B., Lan, L., Jiang D., et al, 2019. Research on
Small Sample Speech Recognition based on Transfer
learning. Journal of Beijing Institute of Printing and
Technology
Xu, D. D., Jiang, Z. X., 2021. End-to-end Speech
Recognition based on HOPE-CTC. Computer
Engineering and Design
Vazhenina, D., Markov, K., 2020. End-to-end noisy speech
recognition using Fourier and Hilbert spectrum features.
Electronics.
Davis, K. H., Bidduloh, R., Balashek, S., 2015. Automatic
Recognition of spoken digits. Journal of the Acoustical
Society of America.