Modeling Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones

Core Concepts

Proposing speech-dependent models for accurate in-ear own voice simulation in hearables.

Abstract

The content discusses the challenges of capturing own voice using in-ear microphones in hearables. It introduces speech-dependent models based on phoneme recognition to improve accuracy. Experimental results show the effectiveness of these models under various conditions. Directory: Introduction Hearables with in-ear microphones capture own voice. Data Extraction and Quotations No key metrics or quotes found. Modeling Own Voice Transfer Characteristics Speech-independent vs. speech-dependent models. System Identification and Simulation Adaptive filtering-based model for time-varying transfer characteristics. Experimental Evaluation Matched, utterance mismatch, and talker mismatch conditions analyzed. Conclusion and Future Work Speech-dependent models outperform speech-independent and adaptive filtering-based models.

Stats

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Quotes

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Key Insights Distilled From

Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones

by Mattes Ohlen... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2310.06554.pdf

Deeper Inquiries

How can the proposed speech-dependent models be applied beyond hearables?

The proposed speech-dependent models, which utilize phoneme-specific relative transfer functions (RTFs) to model own voice transfer characteristics, can have applications beyond hearables. One potential application is in noise cancellation systems where accurate modeling of the own voice transfer path is crucial for effective noise reduction. By incorporating speech-dependent RTFs into noise cancellation algorithms, better suppression of environmental noise while preserving the user's own voice can be achieved. Additionally, these models could be utilized in telecommunication devices to enhance speech quality by accurately capturing and transmitting the user's voice while minimizing background noise.

What are potential drawbacks of relying solely on adaptive filtering-based approaches?

While adaptive filtering-based approaches offer a way to model time-varying behavior in signal processing tasks like estimating own voice signals at an in-ear microphone, there are some drawbacks to consider: Limited Generalization: Adaptive filters trained on specific input data may not generalize well to unseen conditions or different talkers. This lack of generalization ability can lead to reduced performance when faced with variability in input signals. Complexity: The adaptation process requires continuous updates based on incoming data, making it computationally intensive and potentially slower compared to other modeling techniques. Overfitting: There is a risk of overfitting when adapting filters too closely to training data, leading to poor performance on new or diverse datasets.

How might advancements in phoneme recognition technology impact this research?

Advancements in phoneme recognition technology can significantly impact research focused on modeling own voice transfer characteristics for various applications: Improved Model Accuracy: More accurate phoneme recognition allows for better segmentation of speech signals into distinct phonemes, enabling more precise estimation of phoneme-specific RTFs. Enhanced Speech Processing Algorithms: Advanced phoneme recognition algorithms can facilitate the development of sophisticated speech enhancement and reconstruction techniques that rely on detailed information about individual phonemes. Real-time Applications: Faster and more efficient phoneme recognition systems enable real-time implementation of speech-dependent models for immediate feedback or adjustment in applications such as hearing aids or communication devices. By leveraging cutting-edge developments in phonetic analysis technologies, researchers can refine their models and algorithms for enhanced performance and adaptability across a range of scenarios beyond traditional hearable devices.