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Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting.

Martin Wöllmer, Erik Marchi, Stefano Squartini

    Cognitive Neurodynamics
    |September 4, 2012
    PubMed
    Summary

    This study enhances automatic speech recognition (ASR) for noisy, conversational speech by combining histogram equalization and multi-condition training. These methods improve keyword detection accuracy in challenging audio environments.

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    Detection of Amyotrophic Lateral Sclerosis with Computer Audition: An Impact Analysis of Different Speech Tasks.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

    Area of Science:

    • Speech Recognition
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Conversational, emotional, and noisy speech poses challenges for current automatic speech recognition (ASR) systems.
    • Advanced algorithms are needed to improve speech features and models for robust performance.

    Purpose of the Study:

    • To develop robust keyword detection techniques for noisy and conversational speech.
    • To enhance the performance of ASR systems in real-world scenarios.

    Main Methods:

    • Combining histogram equalization with multi-condition training for improved speech feature normalization.
    • Utilizing a multi-stream ASR framework with long short-term memory (LSTM) neural networks to model context-sensitive phoneme estimates.
    • Evaluating techniques on the SEMAINE database, a corpus of emotionally colored conversations.
    Keywords:
    Cognitive agentsHistogram equalizationKeyword spottingLong short-term memoryNeural networks

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    Last Updated: May 19, 2026

    Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
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    Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

    Published on: March 8, 2024

    Main Results:

    • Demonstrated improved robustness in keyword detection under noisy and conversational conditions.
    • Showcased the effectiveness of histogram equalization in reducing speech condition mismatch.
    • Validated the benefits of exploiting contextual information within a multi-stream ASR framework.

    Conclusions:

    • The proposed combination of histogram equalization, multi-condition training, and contextual modeling significantly enhances ASR robustness for challenging speech.
    • The techniques are effective for keyword detection in emotionally colored conversational speech.