Results 51 to 60 of about 6,219 (212)

Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition from Throat Vibrations

Advanced Electronic Materials, EarlyView.
This work presents a throat‐mounted mechanoelectric sensor integrated with an ion‐gel/graphene reservoir device for on‐device speech recognition. The system converts raw biomechanical vibrations into rich nonlinear current dynamics, enabling efficient classification through a simple linear readout. The approach highlights a compact and tunable physical‐
Daiki Nishioka, Akito Tateyama, Hina Kitano, Takashi Nakanishi, Kazuya Terabe, Takashi Tsuchiya   +5 more
wiley   +1 more source

On Compensating the Mel-Frequency Cepstral Coefficients [PDF]

, 2008
This paper describes a novel noise-robust automatic speech recognition (ASR) front-end that employs a combination of Mel-filterbank output compensation and cumulative distribution mapping of cepstral coefficients with truncated Gaussian distribution ...
For Noisy Speech, Eric H. C. Choi
core  

Speech Emotion Recognition Using Cepstral Features Extracted With Gammatone Filter Banks Realized Based on ERB and Mel Frequency Scales

IEEE Access
Speech emotion recognition (SER) involves identifying a speaker’s emotional state from their speech utterance. Prior research has explored various cepstral features for developing SER systems. Among these, Mel-frequency cepstral coefficients (MFCC)
Nagarajan Sugan, Lakshmi Sutha Kumar, Nettimi Satya Sai Srinivas   +2 more
doaj   +1 more source

Soft Active Electromyography Interface for Machine Learning‐Enabled Silent Speech Recognition

Advanced Intelligent Systems, EarlyView.
A soft, hand‐worn electromyography interface enables intent‐driven silent speech recognition without continuous facial attachment. The device integrates liquid‐metal interconnects, a transparent flexible circuit, and elastomer encapsulation with a fingertip electrode that contacts perioral muscles only on demand.
Yuta Kurotaki, Shusuke Yamakoshi, Reitaro Yoshida, Yutaka Isoda, Tamami Takano, Yuji Isano, Yusuke Miyake, Kentaro Kuribayashi, Hiroki Ota   +8 more
wiley   +1 more source

Palmprint recognition based on Mel frequency Cepstral coefficients feature extraction [PDF]

, 2010
Palmprint identification is a measurement of palmprint features for recognizing the identity of a user. Palmprint is universal, easy to capture and it does not change much across time.
Maged M.M. Fahmy, Fahmy, Maged M.M.
core   +1 more source

Passive Acoustic Identification of Social Groups in the Hainan Gibbon

Remote Sensing in Ecology and Conservation, EarlyView.
Passive acoustic monitoring offers a non‐invasive means of assessing visually hard‐to‐survey wildlife species with distinctive vocalizations. We evaluated whether deep learning can identify Hainan gibbon (Nomascus hainanus) social groups from their calls.
Emmanuel Kabuga, Emmanuel Dufourq, Nonhlanhla L. Luphade, Samuel T. Turvey, Heidi Ma, Susan M. Cheyne, Hui Lui, Jessica V. Bryant, Qing Chen, Wenyong Li, Zhiwei Liu, Zhaoli Zhou, Stefan Britz, Bubacarr Bah, Ian Durbach   +14 more
wiley   +1 more source

Speaker identification using higher order spectral phase features and their effectiveness vis-a-vis Mel-Cepstral features [PDF]

, 2004
The effectiveness of higher-order spectral (HOS) phase features in speaker recognition is investigated by comparison with Mel Cepstral features on the same speech data.
Sridharan, Subramanian, Ning, Daryl, Daryl Ning, Sridha Sridharan, Chandran, Vinod, Vinod Chandran   +5 more
core   +1 more source

Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions

World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.
ABSTRACT Objective To provide a comprehensive review of the current landscape of artificial intelligence (AI) applications in voice disorder, with emphasis on emerging applications, limitations, and future directions for clinical integration. Methods Literature review.
Rachel B. Kutler, Anaïs Rameau
wiley   +1 more source

Hardware Accelerator Design of DCT Algorithm With Unique-Group Cosine Coefficients for Mel-Scale Frequency Cepstral Coefficients

IEEE Access, 2022
This study presents a compact $L$ -points discrete cosine transform (DCT) hardware accelerator for $M$ -points Mel-scale Frequency Cepstral Coefficients (MFCC).
Shin-Chi Lai, Ying-Hsiu Hung, Yi-Chang Zhu, Szu-Ting Wang, Qi-Xian Huang, Ming-Hwa Sheu, Wen-Ho Juang   +6 more
doaj   +1 more source

Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps

Revista Facultad de Ingeniería, 2016
This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained
Álvaro David Orjuela-Cañón, Hugo Fernando Posada-Quintero   +1 more
doaj   +1 more source