Speech Recognition: Wideband Speech Recognition Using Feature Extraction Techniques And Supervised Learning

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(as of Dec 28,2024 17:35:54 UTC – Details)

Publisher ‏ : ‎ LAP LAMBERT Academic Publishing (December 17, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 100 pages
ISBN-10 ‏ : ‎ 6200496692
ISBN-13 ‏ : ‎ 978-6200496690
Item Weight ‏ : ‎ 5.9 ounces
Dimensions ‏ : ‎ 5.91 x 0.23 x 8.66 inches

Speech recognition technology has seen significant advancements in recent years, with wideband speech recognition becoming increasingly popular due to its ability to capture more speech information and improve accuracy. In this post, we will delve into the topic of wideband speech recognition using feature extraction techniques and supervised learning.

Feature extraction is a crucial step in speech recognition, as it involves extracting relevant speech features from the audio signal that can be used to distinguish between different phonetic units. In wideband speech recognition, higher frequency speech information is captured, allowing for more detailed feature extraction.

One common feature extraction technique used in wideband speech recognition is Mel-frequency cepstral coefficients (MFCCs). MFCCs are a representation of the short-term power spectrum of a sound, which can effectively capture the spectral characteristics of speech signals. Other feature extraction techniques such as Gammatone filterbanks and linear predictive coding (LPC) can also be used to extract relevant speech features in wideband speech recognition.

Supervised learning algorithms are then applied to the extracted features to train the speech recognition system. Supervised learning involves providing the algorithm with labeled training data, allowing it to learn the mapping between the input features and the corresponding speech units. Popular supervised learning algorithms used in wideband speech recognition include Hidden Markov Models (HMMs), deep neural networks (DNNs), and convolutional neural networks (CNNs).

By combining advanced feature extraction techniques with powerful supervised learning algorithms, wideband speech recognition systems can achieve high levels of accuracy and robustness. These systems have a wide range of applications, from voice-controlled smart devices to automatic speech transcription and language translation.

In conclusion, wideband speech recognition using feature extraction techniques and supervised learning represents a cutting-edge technology that is revolutionizing the way we interact with machines through speech. As research in this field continues to advance, we can expect even more accurate and efficient speech recognition systems to emerge, further enhancing the user experience in various applications.
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