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AdaStreamLite: Environment-adaptive Streaming Speech Recognition on Mobile Devices

Authors:

Junzhao DuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 7, Issue 4

Article No.: 187, Pages 1 - 29

https://doi.org/10.1145/3631460

Published: 12 January 2024 Publication History

Abstract

Streaming speech recognition aims to transcribe speech to text in a streaming manner, providing real-time speech interaction for smartphone users. However, it is not trivial to develop a high-performance streaming speech recognition system purely running on mobile platforms, due to the complex real-world acoustic environments and the limited computational resources of smartphones. Most existing solutions lack the generalization to unseen environments and have difficulty to work with streaming speech. In this paper, we design AdaStreamLite, an environment-adaptive streaming speech recognition tool for smartphones. AdaStreamLite interacts with its surroundings to capture the characteristics of the current acoustic environment to improve the robustness against ambient noise in a lightweight manner. We design an environment representation extractor to model acoustic environments with compact feature vectors, and construct a representation lookup table to improve the generalization of AdaStreamLite to unseen environments. We train our system using large speech datasets publicly available covering different languages. We conduct experiments in a large range of real acoustic environments with different smartphones. The results show that AdaStreamLite outperforms the state-of-the-art methods in terms of recognition accuracy, computational resource consumption and robustness against unseen environments.

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Cited By

Agarwal MUpadhyay DGill KDevliyal S(2024)Emotional Resonance Unleashed by exploring Novel Audio Classification Techniques with Log- Melspectrogram Augmentation2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10687403(1-5)Online publication date: 5-Jun-2024
https://doi.org/10.1109/OTCON60325.2024.10687403
Agarwal MGill KThapliyal NRawat R(2024)Harmonizing Emotions: A Novel Approach to Audio Emotion Classification using Log-Melspectrogram with Augmentation2024 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IoT60841.2024.10550216(1-4)Online publication date: 17-Apr-2024
https://doi.org/10.1109/IC3IoT60841.2024.10550216

Index Terms

AdaStreamLite: Environment-adaptive Streaming Speech Recognition on Mobile Devices
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 7, Issue 4

December 2023

1613 pages

EISSN:2474-9567

DOI:10.1145/3640795

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Copyright © 2024 ACM.

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Published: 12 January 2024

Published in IMWUT Volume 7, Issue 4

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Cited By

Agarwal MUpadhyay DGill KDevliyal S(2024)Emotional Resonance Unleashed by exploring Novel Audio Classification Techniques with Log- Melspectrogram Augmentation2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10687403(1-5)Online publication date: 5-Jun-2024
https://doi.org/10.1109/OTCON60325.2024.10687403
Agarwal MGill KThapliyal NRawat R(2024)Harmonizing Emotions: A Novel Approach to Audio Emotion Classification using Log-Melspectrogram with Augmentation2024 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IoT60841.2024.10550216(1-4)Online publication date: 17-Apr-2024
https://doi.org/10.1109/IC3IoT60841.2024.10550216

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