Review of Modgdf &amp; PNCC Techniques for Features Extraction in Speech Recognition

Alasadi, A. A.; Deshmukh, Ratnadeep R.; Waghmare, Swapnil

doi:10.1109/icecct.2019.8869154

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…As stated by Alasadi and Deshmukh (2018), speech recognition is an important field that is constantly being developed as an interdisciplinary subfield of computational linguistics. Speech recognition creates technology and methods that empower the acknowledgment and understanding of natural language into a computer-understandable language.…”

Section: Speech Technologies: Automatic Speech Recognitionmentioning

confidence: 99%

Preprocessing models for speech technologies

Carriço,

Shulby,

Moniz

2023

rapl

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This paper describes the linguistic preprocessing methods on hybrid systems provided by an Artificial Intelligence (AI) international company, Defined.ai. The startup focuses on providing high-quality data, models, and AI tools. The main goal of this work is to enhance and advance the quality of preprocessing models by applying linguistic knowledge. Thus, we focus on two introductory linguistic models in a speech pipeline: Normalizer and Grapheme-to-Phone (G2P). To do so, two initiatives were conducted in collaboration with the Defined.ai Machine Learning team. The first project focuses on expanding and improving a European Portuguese Normalizer model. The second project covers creating G2P models for two different languages – Swedish and Russian. Results show that having a rule-based approach to the Normalizer and G2P increases its accuracy and performance, representing a significant advantage in improving Defined.ai tools and speech pipelines. Also, with the results obtained on the first project, we improved the normalizer in ease of use by increasing each rule with linguistic knowledge. Accordingly, our research demonstrates the added value of linguistic knowledge in preprocessing models.

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Section: Speech Technologies: Automatic Speech Recognitionmentioning

confidence: 99%

Preprocessing models for speech technologies

Carriço,

Shulby,

Moniz

2023

rapl

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“…The PNCC algorithm is used to extract features for voice recognition. This approach was tested for accuracy and complexity, which resulted in enhanced accuracy due to SNR having a higher value [20,21].…”

Section: Many Commercially Available Automatic Speechmentioning

confidence: 99%

Comparison Between Features Extraction Techniques for Impairments Arabic Speech

Shareef¹,

Al-Irhayim²

2022

(AREJ)

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Automatic Speech Recognition (ASR) is a tough task, with the existence of related noise and high unpredictability in a speech presenting the most severe problems. Especially with regard to the noise of speech impairments, whether due to disability or mispronunciation in children. Extraction of noise-resistant features to compensate for speech degradation due to noise impact has remained a difficult challenge in the last few years. This research investigated the impact of different wavelet generations for extracting speech features, then test the produced dataset from each technique with two types of deep learning techniques : deep long shortterm memory (LSTM) and hyper deep learning model convolutional neural network with long short-term memory (CNN-LSTM). The result shows that the deep long short-term memory of MFCC has reached 93% as an accuracy while in the hyper deep learning model of CNN-LSTM the accuracy of MFCC was 91%, as the highest recorded accuracy which proves that MFCC would be the best feature extraction technique for our developed dataset.

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A retrieval method for encrypted speech based on improved power normalized cepstrum coefficients and perceptual hashing

Zhang

Bai

2022

Multimed Tools Appl

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Review of Modgdf & PNCC Techniques for Features Extraction in Speech Recognition

Cited by 4 publications

References 36 publications

Preprocessing models for speech technologies

Preprocessing models for speech technologies

Comparison Between Features Extraction Techniques for Impairments Arabic Speech

A retrieval method for encrypted speech based on improved power normalized cepstrum coefficients and perceptual hashing

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